Pushing the docs to dev/ for branch: main, commit a530a176ad66725c482feaf1c19e5afe22817139

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 0416b8a1e7c7526b0abe1bceab0e2cc7
+config: 1205457fdb4f17847be1c3a0cd5bb518
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: Alexandre Gramfort <[email protected]>\n#          Mathieu Blondel <[email protected]>\n#          Andreas Mueller <[email protected]>\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import StandardScaler\n\nX, y = datasets.load_digits(return_X_y=True)\n\nX = StandardScaler().fit_transform(X)\n\n# classify small against large digits\ny = (y > 4).astype(int)\n\nl1_ratio = 0.5  # L1 weight in the Elastic-Net regularization\n\nfig, axes = plt.subplots(3, 3)\n\n# Set regularization parameter\nfor i, (C, axes_row) in enumerate(zip((1, 0.1, 0.01), axes)):\n    # Increase tolerance for short training time\n    clf_l1_LR = LogisticRegression(C=C, penalty=\"l1\", tol=0.01, solver=\"saga\")\n    clf_l2_LR = LogisticRegression(C=C, penalty=\"l2\", tol=0.01, solver=\"saga\")\n    clf_en_LR = LogisticRegression(\n        C=C, penalty=\"elasticnet\", solver=\"saga\", l1_ratio=l1_ratio, tol=0.01\n    )\n    clf_l1_LR.fit(X, y)\n    clf_l2_LR.fit(X, y)\n    clf_en_LR.fit(X, y)\n\n    coef_l1_LR = clf_l1_LR.coef_.ravel()\n    coef_l2_LR = clf_l2_LR.coef_.ravel()\n    coef_en_LR = clf_en_LR.coef_.ravel()\n\n    # coef_l1_LR contains zeros due to the\n    # L1 sparsity inducing norm\n\n    sparsity_l1_LR = np.mean(coef_l1_LR == 0) * 100\n    sparsity_l2_LR = np.mean(coef_l2_LR == 0) * 100\n    sparsity_en_LR = np.mean(coef_en_LR == 0) * 100\n\n    print(\"C=%.2f\" % C)\n    print(\"{:<40} {:.2f}%\".format(\"Sparsity with L1 penalty:\", sparsity_l1_LR))\n    print(\"{:<40} {:.2f}%\".format(\"Sparsity with Elastic-Net penalty:\", sparsity_en_LR))\n    print(\"{:<40} {:.2f}%\".format(\"Sparsity with L2 penalty:\", sparsity_l2_LR))\n    print(\"{:<40} {:.2f}\".format(\"Score with L1 penalty:\", clf_l1_LR.score(X, y)))\n    print(\n        \"{:<40} {:.2f}\".format(\"Score with Elastic-Net penalty:\", clf_en_LR.score(X, y))\n    )\n    print(\"{:<40} {:.2f}\".format(\"Score with L2 penalty:\", clf_l2_LR.score(X, y)))\n\n    if i == 0:\n        axes_row[0].set_title(\"L1 penalty\")\n        axes_row[1].set_title(\"Elastic-Net\\nl1_ratio = %s\" % l1_ratio)\n        axes_row[2].set_title(\"L2 penalty\")\n\n    for ax, coefs in zip(axes_row, [coef_l1_LR, coef_en_LR, coef_l2_LR]):\n        ax.imshow(\n            np.abs(coefs.reshape(8, 8)),\n            interpolation=\"nearest\",\n            cmap=\"binary\",\n            vmax=1,\n            vmin=0,\n        )\n        ax.set_xticks(())\n        ax.set_yticks(())\n\n    axes_row[0].set_ylabel(\"C = %s\" % C)\n\nplt.show()"
+        "# Authors: Alexandre Gramfort <[email protected]>\n#          Mathieu Blondel <[email protected]>\n#          Andreas Mueller <[email protected]>\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import StandardScaler\n\nX, y = datasets.load_digits(return_X_y=True)\n\nX = StandardScaler().fit_transform(X)\n\n# classify small against large digits\ny = (y > 4).astype(int)\n\nl1_ratio = 0.5  # L1 weight in the Elastic-Net regularization\n\nfig, axes = plt.subplots(3, 3)\n\n# Set regularization parameter\nfor i, (C, axes_row) in enumerate(zip((1, 0.1, 0.01), axes)):\n    # Increase tolerance for short training time\n    clf_l1_LR = LogisticRegression(C=C, penalty=\"l1\", tol=0.01, solver=\"saga\")\n    clf_l2_LR = LogisticRegression(C=C, penalty=\"l2\", tol=0.01, solver=\"saga\")\n    clf_en_LR = LogisticRegression(\n        C=C, penalty=\"elasticnet\", solver=\"saga\", l1_ratio=l1_ratio, tol=0.01\n    )\n    clf_l1_LR.fit(X, y)\n    clf_l2_LR.fit(X, y)\n    clf_en_LR.fit(X, y)\n\n    coef_l1_LR = clf_l1_LR.coef_.ravel()\n    coef_l2_LR = clf_l2_LR.coef_.ravel()\n    coef_en_LR = clf_en_LR.coef_.ravel()\n\n    # coef_l1_LR contains zeros due to the\n    # L1 sparsity inducing norm\n\n    sparsity_l1_LR = np.mean(coef_l1_LR == 0) * 100\n    sparsity_l2_LR = np.mean(coef_l2_LR == 0) * 100\n    sparsity_en_LR = np.mean(coef_en_LR == 0) * 100\n\n    print(f\"C={C:.2f}\")\n    print(f\"{'Sparsity with L1 penalty:':<40} {sparsity_l1_LR:.2f}%\")\n    print(f\"{'Sparsity with Elastic-Net penalty:':<40} {sparsity_en_LR:.2f}%\")\n    print(f\"{'Sparsity with L2 penalty:':<40} {sparsity_l2_LR:.2f}%\")\n    print(f\"{'Score with L1 penalty:':<40} {clf_l1_LR.score(X, y):.2f}\")\n    print(f\"{'Score with Elastic-Net penalty:':<40} {clf_en_LR.score(X, y):.2f}\")\n    print(f\"{'Score with L2 penalty:':<40} {clf_l2_LR.score(X, y):.2f}\")\n\n    if i == 0:\n        axes_row[0].set_title(\"L1 penalty\")\n        axes_row[1].set_title(\"Elastic-Net\\nl1_ratio = %s\" % l1_ratio)\n        axes_row[2].set_title(\"L2 penalty\")\n\n    for ax, coefs in zip(axes_row, [coef_l1_LR, coef_en_LR, coef_l2_LR]):\n        ax.imshow(\n            np.abs(coefs.reshape(8, 8)),\n            interpolation=\"nearest\",\n            cmap=\"binary\",\n            vmax=1,\n            vmin=0,\n        )\n        ax.set_xticks(())\n        ax.set_yticks(())\n\n    axes_row[0].set_ylabel(f\"C = {C}\")\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -61,15 +61,13 @@
     sparsity_l2_LR = np.mean(coef_l2_LR == 0) * 100
     sparsity_en_LR = np.mean(coef_en_LR == 0) * 100
 
-    print("C=%.2f" % C)
-    print("{:<40} {:.2f}%".format("Sparsity with L1 penalty:", sparsity_l1_LR))
-    print("{:<40} {:.2f}%".format("Sparsity with Elastic-Net penalty:", sparsity_en_LR))
-    print("{:<40} {:.2f}%".format("Sparsity with L2 penalty:", sparsity_l2_LR))
-    print("{:<40} {:.2f}".format("Score with L1 penalty:", clf_l1_LR.score(X, y)))
-    print(
-        "{:<40} {:.2f}".format("Score with Elastic-Net penalty:", clf_en_LR.score(X, y))
-    )
-    print("{:<40} {:.2f}".format("Score with L2 penalty:", clf_l2_LR.score(X, y)))
+    print(f"C={C:.2f}")
+    print(f"{'Sparsity with L1 penalty:':<40} {sparsity_l1_LR:.2f}%")
+    print(f"{'Sparsity with Elastic-Net penalty:':<40} {sparsity_en_LR:.2f}%")
+    print(f"{'Sparsity with L2 penalty:':<40} {sparsity_l2_LR:.2f}%")
+    print(f"{'Score with L1 penalty:':<40} {clf_l1_LR.score(X, y):.2f}")
+    print(f"{'Score with Elastic-Net penalty:':<40} {clf_en_LR.score(X, y):.2f}")
+    print(f"{'Score with L2 penalty:':<40} {clf_l2_LR.score(X, y):.2f}")
 
     if i == 0:
         axes_row[0].set_title("L1 penalty")
@@ -87,6 +85,6 @@
         ax.set_xticks(())
         ax.set_yticks(())
 
-    axes_row[0].set_ylabel("C = %s" % C)
+    axes_row[0].set_ylabel(f"C = {C}")
 
 plt.show()