Pushing the docs to dev/ for branch: main, commit e47b7c0f49450cbbcdb5abcc198cf07719567f34

Author: sklearn-ci

dev/_downloads/02192f99342d6d1323161978b6c80bfc/plot_ols_ridge.zip

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.utils import check_random_state\n\n# Turn down for faster convergence\nt0 = time.time()\ntrain_samples = 5000\n\n# Load data from https://www.openml.org/d/554\nX, y = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)\n\nrandom_state = check_random_state(0)\npermutation = random_state.permutation(X.shape[0])\nX = X[permutation]\ny = y[permutation]\nX = X.reshape((X.shape[0], -1))\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=train_samples, test_size=10000\n)\n\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\n# Turn up tolerance for faster convergence\nclf = LogisticRegression(C=50.0 / train_samples, penalty=\"l1\", solver=\"saga\", tol=0.1)\nclf.fit(X_train, y_train)\nsparsity = np.mean(clf.coef_ == 0) * 100\nscore = clf.score(X_test, y_test)\n# print('Best C % .4f' % clf.C_)\nprint(\"Sparsity with L1 penalty: %.2f%%\" % sparsity)\nprint(\"Test score with L1 penalty: %.4f\" % score)\n\ncoef = clf.coef_.copy()\nplt.figure(figsize=(10, 5))\nscale = np.abs(coef).max()\nfor i in range(10):\n    l1_plot = plt.subplot(2, 5, i + 1)\n    l1_plot.imshow(\n        coef[i].reshape(28, 28),\n        interpolation=\"nearest\",\n        cmap=plt.cm.RdBu,\n        vmin=-scale,\n        vmax=scale,\n    )\n    l1_plot.set_xticks(())\n    l1_plot.set_yticks(())\n    l1_plot.set_xlabel(\"Class %i\" % i)\nplt.suptitle(\"Classification vector for...\")\n\nrun_time = time.time() - t0\nprint(\"Example run in %.3f s\" % run_time)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.utils import check_random_state\n\n# Turn down for faster convergence\nt0 = time.time()\ntrain_samples = 5000\n\n# Load data from https://www.openml.org/d/554\nX, y = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)\n\nrandom_state = check_random_state(0)\npermutation = random_state.permutation(X.shape[0])\nX = X[permutation]\ny = y[permutation]\nX = X.reshape((X.shape[0], -1))\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=train_samples, test_size=10000\n)\n\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\n# Turn up tolerance for faster convergence\nclf = LogisticRegression(C=50.0 / train_samples, penalty=\"l1\", solver=\"saga\", tol=0.1)\nclf.fit(X_train, y_train)\nsparsity = np.mean(clf.coef_ == 0) * 100\nscore = clf.score(X_test, y_test)\n# print('Best C % .4f' % clf.C_)\nprint(\"Sparsity with L1 penalty: %.2f%%\" % sparsity)\nprint(\"Test score with L1 penalty: %.4f\" % score)\n\ncoef = clf.coef_.copy()\nplt.figure(figsize=(10, 5))\nscale = np.abs(coef).max()\nfor i in range(10):\n    l1_plot = plt.subplot(2, 5, i + 1)\n    l1_plot.imshow(\n        coef[i].reshape(28, 28),\n        interpolation=\"nearest\",\n        cmap=plt.cm.RdBu,\n        vmin=-scale,\n        vmax=scale,\n    )\n    l1_plot.set_xticks(())\n    l1_plot.set_yticks(())\n    l1_plot.set_xlabel(f\"Class {i}\")\nplt.suptitle(\"Classification vector for...\")\n\nrun_time = time.time() - t0\nprint(\"Example run in %.3f s\" % run_time)\nplt.show()"
       ]
     }
   ],

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -75,7 +75,7 @@
     )
     l1_plot.set_xticks(())
     l1_plot.set_yticks(())
-    l1_plot.set_xlabel("Class %i" % i)
+    l1_plot.set_xlabel(f"Class {i}")
 plt.suptitle("Classification vector for...")
 
 run_time = time.time() - t0