diff --git a/Ch02-statlearn-lab.ipynb b/Ch02-statlearn-lab.ipynb
index 65a0191..dfebaef 100644
--- a/Ch02-statlearn-lab.ipynb
+++ b/Ch02-statlearn-lab.ipynb
@@ -3550,16 +3550,7 @@
     },
     "lines_to_next_cell": 0
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_3787/1967952672.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
-      "  Auto = pd.read_csv('Auto.data', delim_whitespace=True)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Auto = pd.read_csv('Auto.data', delim_whitespace=True)\n"
    ]
@@ -3709,14 +3700,6 @@
     "lines_to_next_cell": 2
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_3787/134681464.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
-      "  Auto = pd.read_csv('Auto.data',\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
diff --git a/Ch03-linreg-lab.ipynb b/Ch03-linreg-lab.ipynb
index e497df5..2720fcd 100644
--- a/Ch03-linreg-lab.ipynb
+++ b/Ch03-linreg-lab.ipynb
@@ -1485,16 +1485,6 @@
     "lines_to_next_cell": 0
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_846/1591428221.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
-      "  results.params[0],\n",
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_846/1591428221.py:4: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
-      "  results.params[1],\n"
-     ]
-    },
     {
      "data": {
       "image/png": "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",
diff --git a/Ch04-classification-lab.ipynb b/Ch04-classification-lab.ipynb
index 1f6ab76..ef7e1d1 100644
--- a/Ch04-classification-lab.ipynb
+++ b/Ch04-classification-lab.ipynb
@@ -6147,14 +6147,6 @@
     "lines_to_next_cell": 0
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_857/3779905754.py:8: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
-      "  ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n"
-     ]
-    },
     {
      "data": {
       "image/png": "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",
diff --git a/Ch06-varselect-lab.ipynb b/Ch06-varselect-lab.ipynb
index 60225de..765a7fa 100644
--- a/Ch06-varselect-lab.ipynb
+++ b/Ch06-varselect-lab.ipynb
@@ -999,412 +999,6 @@
     "lines_to_next_cell": 0
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -1888,16 +1482,6 @@
     "lines_to_next_cell": 0
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:3: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "<>:3: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_876/874725204.py:3: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "  ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n"
-     ]
-    },
     {
      "data": {
       "image/png": "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",
@@ -2078,14 +1662,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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": [
@@ -2582,14 +2158,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -2641,14 +2209,6 @@
     "lines_to_next_cell": 0
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -2697,238 +2257,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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": [
@@ -3383,1338 +2711,6 @@
     "lines_to_next_cell": 0
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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": [
@@ -5168,16 +3164,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:6: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "<>:6: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_876/3621806029.py:6: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "  ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n"
-     ]
-    },
     {
      "data": {
       "image/png": "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",
@@ -5222,1140 +3208,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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": [
@@ -6843,16 +3695,6 @@
     "lines_to_next_cell": 2
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:5: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "<>:5: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_876/537964502.py:5: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "  ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n"
-     ]
-    },
     {
      "data": {
       "image/png": "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",
@@ -6901,2032 +3743,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15806778.142363887, tolerance: 3759.109166869193\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11005127.926431675, tolerance: 3759.109166869193\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10281669.161078628, tolerance: 3759.109166869193\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9362759.024991756, tolerance: 3759.109166869193\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9171435.130133292, tolerance: 3759.109166869193\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9148311.191396467, tolerance: 3759.109166869193\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9132958.012055231, tolerance: 3759.109166869193\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9114207.980834424, tolerance: 3759.109166869193\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9112382.00859252, tolerance: 3759.109166869193\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9109770.269829823, tolerance: 3759.109166869193\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9107077.714962069, tolerance: 3759.109166869193\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12366168.38748323, tolerance: 4201.186103419478\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11553447.608003614, tolerance: 4201.186103419478\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10217616.440012157, tolerance: 4201.186103419478\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10135989.092876725, tolerance: 4201.186103419478\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9923752.620593691, tolerance: 4201.186103419478\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9612070.387835179, tolerance: 4201.186103419478\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9585514.488134576, tolerance: 4201.186103419478\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9562571.500908706, tolerance: 4201.186103419478\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9542924.549681034, tolerance: 4201.186103419478\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9500529.267319635, tolerance: 4201.186103419478\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9458449.957484357, tolerance: 4201.186103419478\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14111836.23977445, tolerance: 4466.452064951529\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12352853.17507817, tolerance: 4466.452064951529\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10235388.466414697, tolerance: 4466.452064951529\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9893331.203755047, tolerance: 4466.452064951529\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9891716.674948305, tolerance: 4466.452064951529\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9888574.440116653, tolerance: 4466.452064951529\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9887920.67448956, tolerance: 4466.452064951529\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20085874.01890182, tolerance: 4445.102149685068\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12705370.4103452, tolerance: 4445.102149685068\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12236032.779575087, tolerance: 4445.102149685068\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11220921.203073822, tolerance: 4445.102149685068\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10465864.125929631, tolerance: 4445.102149685068\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9959113.332252594, tolerance: 4445.102149685068\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9713747.93315419, tolerance: 4445.102149685068\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9638057.315691985, tolerance: 4445.102149685068\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9634751.672914786, tolerance: 4445.102149685068\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9634302.388158688, tolerance: 4445.102149685068\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9105415.114890344, tolerance: 4436.577708196869\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9072717.557093464, tolerance: 4436.577708196869\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8951612.369533507, tolerance: 4436.577708196869\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8898267.77261921, tolerance: 4436.577708196869\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8896320.890152888, tolerance: 4436.577708196869\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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": [
@@ -9426,16 +4242,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:8: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "<>:8: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_876/1695195493.py:8: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "  ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n"
-     ]
-    },
     {
      "data": {
       "image/png": "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",
@@ -9615,2032 +4421,6 @@
     "lines_to_next_cell": 0
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9046478.453631118, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8783339.10743256, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8658509.901020335, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7944335.999206972, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7530031.627469168, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7442009.746564689, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7361129.146973606, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7287410.635336244, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7220681.095616929, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7106851.483766066, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7058900.769700954, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6890688.792446573, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6868535.393319951, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6849422.765039898, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6797699.628345769, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6782944.868629455, tolerance: 3200.6325551004925\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9870012.667698368, tolerance: 3034.7626598069196\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8834727.194341874, tolerance: 3034.7626598069196\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8319788.946660183, tolerance: 3034.7626598069196\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7663566.80208477, tolerance: 3034.7626598069196\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7183235.551674369, tolerance: 3034.7626598069196\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7061872.01272655, tolerance: 3034.7626598069196\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6982061.123036068, tolerance: 3034.7626598069196\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6869581.8539127605, tolerance: 3034.7626598069196\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6867829.838852356, tolerance: 3034.7626598069196\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6863104.834999975, tolerance: 3034.7626598069196\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8189695.129107313, tolerance: 3200.070250165819\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8016371.614804336, tolerance: 3200.070250165819\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7861511.843847991, tolerance: 3200.070250165819\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7186386.647782043, tolerance: 3200.070250165819\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7154889.512672322, tolerance: 3200.070250165819\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7150045.262096588, tolerance: 3200.070250165819\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7143404.8053056635, tolerance: 3200.070250165819\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553002.845594909, tolerance: 2753.321903486231\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7639277.052384096, tolerance: 2753.321903486231\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7090358.56776332, tolerance: 2753.321903486231\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6902628.941757251, tolerance: 2753.321903486231\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6711786.944628098, tolerance: 2753.321903486231\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6524688.87351594, tolerance: 2753.321903486231\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6510538.426567691, tolerance: 2753.321903486231\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6489074.07424213, tolerance: 2753.321903486231\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6485555.163897057, tolerance: 2753.321903486231\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8947015.008946367, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7295907.831051515, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6627701.871803499, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6269768.629955587, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5703294.550898108, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5481282.987854579, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4968593.444998967, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4923922.319001437, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4725366.49534371, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4671535.66214718, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4662395.341810633, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4658952.253038207, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4656121.776081925, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4653809.600037623, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4649188.052146212, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4648205.237515733, tolerance: 3224.823681413525\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
-      "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:683: 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/jtaylo/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did 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"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -11769,16 +4549,6 @@
     "lines_to_next_cell": 0
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:4: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "<>:4: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_876/163252521.py:4: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "  ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n"
-     ]
-    },
     {
      "data": {
       "image/png": "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",
@@ -11857,16 +4627,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:7: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "<>:7: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_876/3184589275.py:7: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "  ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n"
-     ]
-    },
     {
      "data": {
       "image/png": "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",
diff --git a/Ch07-nonlin-lab.ipynb b/Ch07-nonlin-lab.ipynb
index afbabc6..7534f6d 100644
--- a/Ch07-nonlin-lab.ipynb
+++ b/Ch07-nonlin-lab.ipynb
@@ -1866,16 +1866,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:11: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "<>:11: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_883/1121239147.py:11: SyntaxWarning: invalid escape sequence '\\l'\n",
-      "  ax.legend(title='$\\lambda$');\n"
-     ]
-    },
     {
      "data": {
       "image/png": "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",
@@ -2775,20 +2765,6 @@
       "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
       "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_883/2135516388.py:1: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
-      " \n",
-      "Please do not make inferences based on these values! \n",
-      "\n",
-      "Collaborate on a solution, and stay up to date at: \n",
-      "github.com/dswah/pyGAM/issues/163 \n",
-      "\n",
-      "  gam_full.summary()\n"
-     ]
     }
    ],
    "source": [
diff --git a/Ch09-svm-lab.ipynb b/Ch09-svm-lab.ipynb
index 1b2ae7c..ff1be02 100644
--- a/Ch09-svm-lab.ipynb
+++ b/Ch09-svm-lab.ipynb
@@ -2208,16 +2208,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:9: SyntaxWarning: invalid escape sequence '\\g'\n",
-      "<>:9: SyntaxWarning: invalid escape sequence '\\g'\n",
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_904/3619777199.py:9: SyntaxWarning: invalid escape sequence '\\g'\n",
-      "  name='Training $\\gamma=50$',\n"
-     ]
-    },
     {
      "data": {
       "image/png": "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",
@@ -2266,18 +2256,7 @@
      "shell.execute_reply": "2024-06-04T23:19:52.757029Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:4: SyntaxWarning: invalid escape sequence '\\g'\n",
-      "<>:4: SyntaxWarning: invalid escape sequence '\\g'\n",
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_904/1317387791.py:4: SyntaxWarning: invalid escape sequence '\\g'\n",
-      "  name='Test $\\gamma=50$',\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "roc_curve(svm_flex,\n",
     "          X_test,\n",
diff --git a/Ch10-deeplearning-lab.ipynb b/Ch10-deeplearning-lab.ipynb
index 8b6df2a..2123131 100644
--- a/Ch10-deeplearning-lab.ipynb
+++ b/Ch10-deeplearning-lab.ipynb
@@ -899,357 +899,357 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 0: 100%|█████████████████████████████████████████████████████████████████████████████| 6/6 [00:01<00:00,  4.23it/s, v_num=4]\n",
+      "Epoch 0: 100%|█████████████████████████████████████████████████████████████████████████████| 6/6 [00:01<00:00,  4.36it/s, v_num=5]\n",
       "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
       "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
       "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 667.46it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 466.29it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 183.31it/s]\u001b[A\n",
-      "Epoch 1: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 212.93it/s, v_num=4]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 875.64it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 608.27it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 193.69it/s]\u001b[A\n",
+      "Epoch 1: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 220.32it/s, v_num=5]\u001b[A\n",
       "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
       "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
       "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 774.00it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 591.00it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 583.76it/s]\u001b[A\n",
-      "Epoch 2: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 237.13it/s, v_num=4]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 786.78it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 612.58it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 558.55it/s]\u001b[A\n",
+      "Epoch 2: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 237.66it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 780.05it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 572.44it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 561.06it/s]\u001b[A\n",
+      "Epoch 3: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 122.40it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 744.60it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 547.95it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 538.03it/s]\u001b[A\n",
+      "Epoch 4: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 240.57it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 728.30it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 553.56it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 552.58it/s]\u001b[A\n",
+      "Epoch 5: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 239.62it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 660.10it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 551.74it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 546.87it/s]\u001b[A\n",
+      "Epoch 6: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 242.95it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 766.78it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 568.18it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 578.13it/s]\u001b[A\n",
+      "Epoch 7: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 243.68it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 694.88it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 554.14it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 552.27it/s]\u001b[A\n",
+      "Epoch 8: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 230.89it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 802.58it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 635.50it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 571.64it/s]\u001b[A\n",
+      "Epoch 9: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 234.76it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 172.47it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 229.02it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 281.93it/s]\u001b[A\n",
+      "Epoch 10: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 218.17it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 785.45it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 604.76it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 606.05it/s]\u001b[A\n",
+      "Epoch 11: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 239.51it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 682.00it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 571.74it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 555.34it/s]\u001b[A\n",
+      "Epoch 12: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 236.39it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 838.19it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 667.30it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 264.80it/s]\u001b[A\n",
+      "Epoch 13: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 226.14it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 800.59it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 584.98it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 548.25it/s]\u001b[A\n",
+      "Epoch 14: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 240.28it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 793.62it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 612.93it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 592.89it/s]\u001b[A\n",
+      "Epoch 15: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 227.43it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 684.11it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 598.29it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 582.65it/s]\u001b[A\n",
+      "Epoch 16: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 125.53it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 488.51it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 451.68it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 473.65it/s]\u001b[A\n",
+      "Epoch 17: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 218.53it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 724.78it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 550.54it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 528.65it/s]\u001b[A\n",
+      "Epoch 18: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 234.04it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████▎                                              | 1/3 [00:00<00:00, 99.52it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 119.83it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 160.18it/s]\u001b[A\n",
+      "Epoch 19: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 227.70it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 858.43it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 599.70it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 548.63it/s]\u001b[A\n",
+      "Epoch 20: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 223.23it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 885.81it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 687.99it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 646.57it/s]\u001b[A\n",
+      "Epoch 21: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 234.21it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 780.63it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 594.89it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 553.41it/s]\u001b[A\n",
+      "Epoch 22: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 230.33it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 749.12it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 638.21it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 584.82it/s]\u001b[A\n",
+      "Epoch 23: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 243.22it/s, v_num=5]\u001b[A\n",
       "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
       "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
       "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
       "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 796.79it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 592.75it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 569.26it/s]\u001b[A\n",
-      "Epoch 3: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 239.46it/s, v_num=4]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 642.26it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 575.82it/s]\u001b[A\n",
+      "Epoch 24: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 116.96it/s, v_num=5]\u001b[A\n",
       "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
       "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
       "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 702.80it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 526.36it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 535.83it/s]\u001b[A\n",
-      "Epoch 4: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 237.87it/s, v_num=4]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 235.01it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 294.89it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 325.20it/s]\u001b[A\n",
+      "Epoch 25: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 221.34it/s, v_num=5]\u001b[A\n",
       "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
       "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
       "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 672.38it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 865.16it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 613.52it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 567.56it/s]\u001b[A\n",
+      "Epoch 26: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 228.62it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 693.96it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 530.25it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 544.29it/s]\u001b[A\n",
+      "Epoch 27: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 229.37it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 690.19it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 578.56it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 548.85it/s]\u001b[A\n",
+      "Epoch 28: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 236.54it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 744.60it/s]\u001b[A\n",
       "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 538.94it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 548.73it/s]\u001b[A\n",
-      "Epoch 5: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 236.98it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 658.76it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 554.77it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 526.48it/s]\u001b[A\n",
-      "Epoch 6: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 217.83it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 771.01it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 619.18it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 584.54it/s]\u001b[A\n",
-      "Epoch 7: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 234.52it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 723.16it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 595.06it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 589.28it/s]\u001b[A\n",
-      "Epoch 8: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 239.80it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 756.96it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 564.02it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 557.51it/s]\u001b[A\n",
-      "Epoch 9: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 248.40it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 147.51it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████▋                       | 2/3 [00:00<00:00, 81.29it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 110.30it/s]\u001b[A\n",
-      "Epoch 10: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 226.68it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 713.68it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 563.07it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 579.94it/s]\u001b[A\n",
-      "Epoch 11: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 232.75it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 722.66it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 539.25it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 551.57it/s]\u001b[A\n",
-      "Epoch 12: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 227.37it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 659.17it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 496.43it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 496.21it/s]\u001b[A\n",
-      "Epoch 13: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 238.17it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 602.72it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 534.71it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 532.86it/s]\u001b[A\n",
-      "Epoch 14: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 131.89it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 558.72it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 453.51it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 500.33it/s]\u001b[A\n",
-      "Epoch 15: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 231.68it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 911.41it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 719.68it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 673.24it/s]\u001b[A\n",
-      "Epoch 16: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 236.04it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 686.24it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 539.67it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 527.87it/s]\u001b[A\n",
-      "Epoch 17: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 232.51it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 745.12it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 571.39it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 561.36it/s]\u001b[A\n",
-      "Epoch 18: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 231.37it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 821.12it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 605.15it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 600.24it/s]\u001b[A\n",
-      "Epoch 19: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 118.17it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 761.63it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 568.68it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 576.48it/s]\u001b[A\n",
-      "Epoch 20: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 235.80it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 909.83it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 663.97it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 599.27it/s]\u001b[A\n",
-      "Epoch 21: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 120.08it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 636.95it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 529.38it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 523.92it/s]\u001b[A\n",
-      "Epoch 22: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 231.59it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 790.63it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 569.34it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 549.35it/s]\u001b[A\n",
-      "Epoch 23: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 250.38it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 764.97it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 574.72it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 537.43it/s]\u001b[A\n",
-      "Epoch 24: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 224.36it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 508.89it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████▋                       | 2/3 [00:00<00:00, 64.37it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|██████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 86.19it/s]\u001b[A\n",
-      "Epoch 25: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 208.13it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 843.42it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 612.58it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 586.18it/s]\u001b[A\n",
-      "Epoch 26: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 224.28it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 738.56it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 554.62it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 548.73it/s]\u001b[A\n",
-      "Epoch 27: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 229.33it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 781.21it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 523.96it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 487.58it/s]\u001b[A\n",
-      "Epoch 28: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 158.83it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 589.34it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 519.77it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 509.62it/s]\u001b[A\n",
-      "Epoch 29: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 226.07it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 938.95it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 658.55it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 626.33it/s]\u001b[A\n",
-      "Epoch 30: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 236.06it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 779.47it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 618.81it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 590.47it/s]\u001b[A\n",
-      "Epoch 31: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 235.79it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 716.85it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 569.11it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 555.54it/s]\u001b[A\n",
-      "Epoch 32: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 235.95it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 767.63it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 601.33it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 577.73it/s]\u001b[A\n",
-      "Epoch 33: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 116.79it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 683.22it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 528.98it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 523.94it/s]\u001b[A\n",
-      "Epoch 34: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 205.11it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 719.93it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 618.40it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 598.19it/s]\u001b[A\n",
-      "Epoch 35: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 228.92it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 758.74it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 649.57it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 609.02it/s]\u001b[A\n",
-      "Epoch 36: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 231.79it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 803.04it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 670.61it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 629.96it/s]\u001b[A\n",
-      "Epoch 37: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 232.50it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 683.11it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 596.12it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 560.44it/s]\u001b[A\n",
-      "Epoch 38: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 111.57it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 743.01it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 561.49it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 539.00it/s]\u001b[A\n",
-      "Epoch 39: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 247.52it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 710.66it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 521.91it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 548.54it/s]\u001b[A\n",
-      "Epoch 40: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 236.49it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 642.71it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 519.35it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 539.00it/s]\u001b[A\n",
-      "Epoch 41: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 208.79it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 698.82it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 575.71it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 567.87it/s]\u001b[A\n",
-      "Epoch 42: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 251.65it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 793.02it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 587.36it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 562.42it/s]\u001b[A\n",
-      "Epoch 43: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 255.68it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 727.93it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 592.79it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 571.53it/s]\u001b[A\n",
-      "Epoch 44: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 123.65it/s, v_num=4]\u001b[A\n",
-      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
-      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 307.12it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 329.77it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 383.10it/s]\u001b[A\n",
-      "Epoch 45: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 238.98it/s, v_num=4]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 528.80it/s]\u001b[A\n",
+      "Epoch 29: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 111.20it/s, v_num=5]\u001b[A\n",
       "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
       "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
       "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
       "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 745.26it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 562.77it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 558.37it/s]\u001b[A\n",
-      "Epoch 46: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 237.44it/s, v_num=4]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 645.63it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 602.05it/s]\u001b[A\n",
+      "Epoch 30: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 231.35it/s, v_num=5]\u001b[A\n",
       "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
       "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
       "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 807.84it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 634.54it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 605.59it/s]\u001b[A\n",
-      "Epoch 47: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 238.24it/s, v_num=4]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 784.28it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 641.48it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 599.53it/s]\u001b[A\n",
+      "Epoch 31: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 230.82it/s, v_num=5]\u001b[A\n",
       "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
       "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
       "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 772.72it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 548.96it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 539.07it/s]\u001b[A\n",
-      "Epoch 48: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 238.45it/s, v_num=4]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 738.04it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 599.91it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 554.46it/s]\u001b[A\n",
+      "Epoch 32: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 221.98it/s, v_num=5]\u001b[A\n",
       "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
       "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
       "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 740.26it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 614.28it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 684.11it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 539.70it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 540.16it/s]\u001b[A\n",
+      "Epoch 33: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 229.83it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 772.15it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 540.96it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 547.04it/s]\u001b[A\n",
+      "Epoch 34: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 109.46it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 771.58it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 572.87it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 570.03it/s]\u001b[A\n",
+      "Epoch 35: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 228.10it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 823.87it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 576.70it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 564.86it/s]\u001b[A\n",
+      "Epoch 36: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 243.33it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 829.90it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 652.76it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 612.13it/s]\u001b[A\n",
+      "Epoch 37: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 237.57it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 799.98it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 598.42it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 550.84it/s]\u001b[A\n",
+      "Epoch 38: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 232.75it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████▎                                              | 1/3 [00:00<00:00, 41.24it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████▋                       | 2/3 [00:00<00:00, 70.34it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|██████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 98.42it/s]\u001b[A\n",
+      "Epoch 39: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 226.21it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 761.08it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 576.89it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 582.08it/s]\u001b[A\n",
+      "Epoch 40: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 232.51it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 810.34it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 597.35it/s]\u001b[A\n",
       "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 598.56it/s]\u001b[A\n",
-      "Epoch 49: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 114.04it/s, v_num=4]\u001b[A\n",
+      "Epoch 41: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 226.77it/s, v_num=5]\u001b[A\n",
       "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
       "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
       "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
-      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 766.22it/s]\u001b[A\n",
-      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 561.98it/s]\u001b[A\n",
-      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 563.60it/s]\u001b[A\n",
-      "Epoch 49: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 89.61it/s, v_num=4]\u001b[A"
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 791.23it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 640.65it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 601.59it/s]\u001b[A\n",
+      "Epoch 42: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 237.22it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 874.91it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 613.83it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 570.34it/s]\u001b[A\n",
+      "Epoch 43: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 112.53it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 685.90it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 589.29it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 558.89it/s]\u001b[A\n",
+      "Epoch 44: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 242.76it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 874.18it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 650.83it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 632.50it/s]\u001b[A\n",
+      "Epoch 45: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 223.47it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 931.86it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 755.53it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 713.76it/s]\u001b[A\n",
+      "Epoch 46: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 226.72it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 811.59it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 663.97it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 605.21it/s]\u001b[A\n",
+      "Epoch 47: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 234.68it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 838.19it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 659.22it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 606.17it/s]\u001b[A\n",
+      "Epoch 48: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 107.08it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 841.05it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 659.84it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 591.94it/s]\u001b[A\n",
+      "Epoch 49: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 217.71it/s, v_num=5]\u001b[A\n",
+      "Validation: |                                                                                               | 0/? [00:00<?, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                           | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                              | 0/3 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|███████████████████████                                              | 1/3 [00:00<00:00, 791.68it/s]\u001b[A\n",
+      "Validation DataLoader 0:  67%|██████████████████████████████████████████████                       | 2/3 [00:00<00:00, 606.99it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 572.39it/s]\u001b[A\n",
+      "Epoch 49: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 145.86it/s, v_num=5]\u001b[A"
      ]
     },
     {
@@ -1263,7 +1263,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 49: 100%|████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 83.46it/s, v_num=4]\n"
+      "Epoch 49: 100%|███████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 129.66it/s, v_num=5]\n"
      ]
     }
    ],
@@ -1303,7 +1303,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Testing DataLoader 0: 100%|████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 298.15it/s]\n"
+      "Testing DataLoader 0: 100%|████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 329.17it/s]\n"
      ]
     },
     {
diff --git a/Ch11-surv-lab.ipynb b/Ch11-surv-lab.ipynb
index 08b21c8..a97dbac 100644
--- a/Ch11-surv-lab.ipynb
+++ b/Ch11-surv-lab.ipynb
@@ -340,14 +340,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_910/380004221.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
-      "  for sex, df in BrainCancer.groupby('sex'):\n"
-     ]
-    },
     {
      "data": {
       "image/png": "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",
@@ -2071,14 +2063,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_910/1365832640.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
-      "  for center, df in D.groupby('Center'):\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -2131,14 +2115,6 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_910/957073325.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
-      "  for time, df in D.groupby('Time'):\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [