Pushing the docs to dev/ for branch: main, commit efede4b1864492c18238f60ffc39e9729672382c

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -31,59 +31,59 @@
 eps = 5e-3  # the smaller it is the longer is the path
 
 print("Computing regularization path using the lasso...")
-alphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps=eps, fit_intercept=False)
+alphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps=eps)
 
 print("Computing regularization path using the positive lasso...")
 alphas_positive_lasso, coefs_positive_lasso, _ = lasso_path(
-    X, y, eps=eps, positive=True, fit_intercept=False)
+    X, y, eps=eps, positive=True
+)
 print("Computing regularization path using the elastic net...")
-alphas_enet, coefs_enet, _ = enet_path(
-    X, y, eps=eps, l1_ratio=0.8, fit_intercept=False)
+alphas_enet, coefs_enet, _ = enet_path(X, y, eps=eps, l1_ratio=0.8)
 
 print("Computing regularization path using the positive elastic net...")
 alphas_positive_enet, coefs_positive_enet, _ = enet_path(
-    X, y, eps=eps, l1_ratio=0.8, positive=True, fit_intercept=False)
+    X, y, eps=eps, l1_ratio=0.8, positive=True
+)
 
 # Display results
 
 plt.figure(1)
-colors = cycle(['b', 'r', 'g', 'c', 'k'])
+colors = cycle(["b", "r", "g", "c", "k"])
 neg_log_alphas_lasso = -np.log10(alphas_lasso)
 neg_log_alphas_enet = -np.log10(alphas_enet)
 for coef_l, coef_e, c in zip(coefs_lasso, coefs_enet, colors):
     l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)
-    l2 = plt.plot(neg_log_alphas_enet, coef_e, linestyle='--', c=c)
+    l2 = plt.plot(neg_log_alphas_enet, coef_e, linestyle="--", c=c)
 
-plt.xlabel('-Log(alpha)')
-plt.ylabel('coefficients')
-plt.title('Lasso and Elastic-Net Paths')
-plt.legend((l1[-1], l2[-1]), ('Lasso', 'Elastic-Net'), loc='lower left')
-plt.axis('tight')
+plt.xlabel("-Log(alpha)")
+plt.ylabel("coefficients")
+plt.title("Lasso and Elastic-Net Paths")
+plt.legend((l1[-1], l2[-1]), ("Lasso", "Elastic-Net"), loc="lower left")
+plt.axis("tight")
 
 
 plt.figure(2)
 neg_log_alphas_positive_lasso = -np.log10(alphas_positive_lasso)
 for coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors):
     l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)
-    l2 = plt.plot(neg_log_alphas_positive_lasso, coef_pl, linestyle='--', c=c)
+    l2 = plt.plot(neg_log_alphas_positive_lasso, coef_pl, linestyle="--", c=c)
 
-plt.xlabel('-Log(alpha)')
-plt.ylabel('coefficients')
-plt.title('Lasso and positive Lasso')
-plt.legend((l1[-1], l2[-1]), ('Lasso', 'positive Lasso'), loc='lower left')
-plt.axis('tight')
+plt.xlabel("-Log(alpha)")
+plt.ylabel("coefficients")
+plt.title("Lasso and positive Lasso")
+plt.legend((l1[-1], l2[-1]), ("Lasso", "positive Lasso"), loc="lower left")
+plt.axis("tight")
 
 
 plt.figure(3)
 neg_log_alphas_positive_enet = -np.log10(alphas_positive_enet)
 for (coef_e, coef_pe, c) in zip(coefs_enet, coefs_positive_enet, colors):
     l1 = plt.plot(neg_log_alphas_enet, coef_e, c=c)
-    l2 = plt.plot(neg_log_alphas_positive_enet, coef_pe, linestyle='--', c=c)
-
-plt.xlabel('-Log(alpha)')
-plt.ylabel('coefficients')
-plt.title('Elastic-Net and positive Elastic-Net')
-plt.legend((l1[-1], l2[-1]), ('Elastic-Net', 'positive Elastic-Net'),
-           loc='lower left')
-plt.axis('tight')
+    l2 = plt.plot(neg_log_alphas_positive_enet, coef_pe, linestyle="--", c=c)
+
+plt.xlabel("-Log(alpha)")
+plt.ylabel("coefficients")
+plt.title("Elastic-Net and positive Elastic-Net")
+plt.legend((l1[-1], l2[-1]), ("Elastic-Net", "positive Elastic-Net"), loc="lower left")
+plt.axis("tight")
 plt.show()

dev/_downloads/1f682002d8b68c290d9d03599368e83d/plot_lasso_coordinate_descent_path.py

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n# Author: Alexandre Gramfort <[email protected]>\n# License: BSD 3 clause\n\nfrom itertools import cycle\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.linear_model import lasso_path, enet_path\nfrom sklearn import datasets\n\n\nX, y = datasets.load_diabetes(return_X_y=True)\n\n\nX /= X.std(axis=0)  # Standardize data (easier to set the l1_ratio parameter)\n\n# Compute paths\n\neps = 5e-3  # the smaller it is the longer is the path\n\nprint(\"Computing regularization path using the lasso...\")\nalphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps=eps, fit_intercept=False)\n\nprint(\"Computing regularization path using the positive lasso...\")\nalphas_positive_lasso, coefs_positive_lasso, _ = lasso_path(\n    X, y, eps=eps, positive=True, fit_intercept=False)\nprint(\"Computing regularization path using the elastic net...\")\nalphas_enet, coefs_enet, _ = enet_path(\n    X, y, eps=eps, l1_ratio=0.8, fit_intercept=False)\n\nprint(\"Computing regularization path using the positive elastic net...\")\nalphas_positive_enet, coefs_positive_enet, _ = enet_path(\n    X, y, eps=eps, l1_ratio=0.8, positive=True, fit_intercept=False)\n\n# Display results\n\nplt.figure(1)\ncolors = cycle(['b', 'r', 'g', 'c', 'k'])\nneg_log_alphas_lasso = -np.log10(alphas_lasso)\nneg_log_alphas_enet = -np.log10(alphas_enet)\nfor coef_l, coef_e, c in zip(coefs_lasso, coefs_enet, colors):\n    l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)\n    l2 = plt.plot(neg_log_alphas_enet, coef_e, linestyle='--', c=c)\n\nplt.xlabel('-Log(alpha)')\nplt.ylabel('coefficients')\nplt.title('Lasso and Elastic-Net Paths')\nplt.legend((l1[-1], l2[-1]), ('Lasso', 'Elastic-Net'), loc='lower left')\nplt.axis('tight')\n\n\nplt.figure(2)\nneg_log_alphas_positive_lasso = -np.log10(alphas_positive_lasso)\nfor coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors):\n    l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)\n    l2 = plt.plot(neg_log_alphas_positive_lasso, coef_pl, linestyle='--', c=c)\n\nplt.xlabel('-Log(alpha)')\nplt.ylabel('coefficients')\nplt.title('Lasso and positive Lasso')\nplt.legend((l1[-1], l2[-1]), ('Lasso', 'positive Lasso'), loc='lower left')\nplt.axis('tight')\n\n\nplt.figure(3)\nneg_log_alphas_positive_enet = -np.log10(alphas_positive_enet)\nfor (coef_e, coef_pe, c) in zip(coefs_enet, coefs_positive_enet, colors):\n    l1 = plt.plot(neg_log_alphas_enet, coef_e, c=c)\n    l2 = plt.plot(neg_log_alphas_positive_enet, coef_pe, linestyle='--', c=c)\n\nplt.xlabel('-Log(alpha)')\nplt.ylabel('coefficients')\nplt.title('Elastic-Net and positive Elastic-Net')\nplt.legend((l1[-1], l2[-1]), ('Elastic-Net', 'positive Elastic-Net'),\n           loc='lower left')\nplt.axis('tight')\nplt.show()"
+        "print(__doc__)\n\n# Author: Alexandre Gramfort <[email protected]>\n# License: BSD 3 clause\n\nfrom itertools import cycle\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.linear_model import lasso_path, enet_path\nfrom sklearn import datasets\n\n\nX, y = datasets.load_diabetes(return_X_y=True)\n\n\nX /= X.std(axis=0)  # Standardize data (easier to set the l1_ratio parameter)\n\n# Compute paths\n\neps = 5e-3  # the smaller it is the longer is the path\n\nprint(\"Computing regularization path using the lasso...\")\nalphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps=eps)\n\nprint(\"Computing regularization path using the positive lasso...\")\nalphas_positive_lasso, coefs_positive_lasso, _ = lasso_path(\n    X, y, eps=eps, positive=True\n)\nprint(\"Computing regularization path using the elastic net...\")\nalphas_enet, coefs_enet, _ = enet_path(X, y, eps=eps, l1_ratio=0.8)\n\nprint(\"Computing regularization path using the positive elastic net...\")\nalphas_positive_enet, coefs_positive_enet, _ = enet_path(\n    X, y, eps=eps, l1_ratio=0.8, positive=True\n)\n\n# Display results\n\nplt.figure(1)\ncolors = cycle([\"b\", \"r\", \"g\", \"c\", \"k\"])\nneg_log_alphas_lasso = -np.log10(alphas_lasso)\nneg_log_alphas_enet = -np.log10(alphas_enet)\nfor coef_l, coef_e, c in zip(coefs_lasso, coefs_enet, colors):\n    l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)\n    l2 = plt.plot(neg_log_alphas_enet, coef_e, linestyle=\"--\", c=c)\n\nplt.xlabel(\"-Log(alpha)\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Lasso and Elastic-Net Paths\")\nplt.legend((l1[-1], l2[-1]), (\"Lasso\", \"Elastic-Net\"), loc=\"lower left\")\nplt.axis(\"tight\")\n\n\nplt.figure(2)\nneg_log_alphas_positive_lasso = -np.log10(alphas_positive_lasso)\nfor coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors):\n    l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)\n    l2 = plt.plot(neg_log_alphas_positive_lasso, coef_pl, linestyle=\"--\", c=c)\n\nplt.xlabel(\"-Log(alpha)\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Lasso and positive Lasso\")\nplt.legend((l1[-1], l2[-1]), (\"Lasso\", \"positive Lasso\"), loc=\"lower left\")\nplt.axis(\"tight\")\n\n\nplt.figure(3)\nneg_log_alphas_positive_enet = -np.log10(alphas_positive_enet)\nfor (coef_e, coef_pe, c) in zip(coefs_enet, coefs_positive_enet, colors):\n    l1 = plt.plot(neg_log_alphas_enet, coef_e, c=c)\n    l2 = plt.plot(neg_log_alphas_positive_enet, coef_pe, linestyle=\"--\", c=c)\n\nplt.xlabel(\"-Log(alpha)\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Elastic-Net and positive Elastic-Net\")\nplt.legend((l1[-1], l2[-1]), (\"Elastic-Net\", \"positive Elastic-Net\"), loc=\"lower left\")\nplt.axis(\"tight\")\nplt.show()"
       ]
     }
   ],