Pushing the docs to dev/ for branch: main, commit a6e1d2fdf7d1f3e6901819c91456bb67c405c646

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: Tom Dupre la Tour <[email protected]>\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.multiclass import OneVsRestClassifier\n\n# make 3-class dataset for classification\ncenters = [[-5, 0], [0, 1.5], [5, -1]]\nX, y = make_blobs(n_samples=1000, centers=centers, random_state=40)\ntransformation = [[0.4, 0.2], [-0.4, 1.2]]\nX = np.dot(X, transformation)\n\nfor multi_class in (\"multinomial\", \"ovr\"):\n    clf = LogisticRegression(solver=\"sag\", max_iter=100, random_state=42)\n    if multi_class == \"ovr\":\n        clf = OneVsRestClassifier(clf)\n    clf.fit(X, y)\n\n    # print the training scores\n    print(\"training score : %.3f (%s)\" % (clf.score(X, y), multi_class))\n\n    _, ax = plt.subplots()\n    DecisionBoundaryDisplay.from_estimator(\n        clf, X, response_method=\"predict\", cmap=plt.cm.Paired, ax=ax\n    )\n    plt.title(\"Decision surface of LogisticRegression (%s)\" % multi_class)\n    plt.axis(\"tight\")\n\n    # Plot also the training points\n    colors = \"bry\"\n    for i, color in zip(clf.classes_, colors):\n        idx = np.where(y == i)\n        plt.scatter(\n            X[idx, 0], X[idx, 1], c=color, cmap=plt.cm.Paired, edgecolor=\"black\", s=20\n        )\n\n    # Plot the three one-against-all classifiers\n    xmin, xmax = plt.xlim()\n    ymin, ymax = plt.ylim()\n    if multi_class == \"ovr\":\n        coef = np.concatenate([est.coef_ for est in clf.estimators_])\n        intercept = np.concatenate([est.intercept_ for est in clf.estimators_])\n    else:\n        coef = clf.coef_\n        intercept = clf.intercept_\n\n    def plot_hyperplane(c, color):\n        def line(x0):\n            return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]\n\n        plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls=\"--\", color=color)\n\n    for i, color in zip(clf.classes_, colors):\n        plot_hyperplane(i, color)\n\nplt.show()"
+        "# Authors: Tom Dupre la Tour <[email protected]>\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.multiclass import OneVsRestClassifier\n\n# make 3-class dataset for classification\ncenters = [[-5, 0], [0, 1.5], [5, -1]]\nX, y = make_blobs(n_samples=1000, centers=centers, random_state=40)\ntransformation = [[0.4, 0.2], [-0.4, 1.2]]\nX = np.dot(X, transformation)\n\nfor multi_class in (\"multinomial\", \"ovr\"):\n    clf = LogisticRegression(solver=\"sag\", max_iter=100, random_state=42)\n    if multi_class == \"ovr\":\n        clf = OneVsRestClassifier(clf)\n    clf.fit(X, y)\n\n    # print the training scores\n    print(\"training score : %.3f (%s)\" % (clf.score(X, y), multi_class))\n\n    _, ax = plt.subplots()\n    DecisionBoundaryDisplay.from_estimator(\n        clf, X, response_method=\"predict\", cmap=plt.cm.Paired, ax=ax\n    )\n    plt.title(\"Decision surface of LogisticRegression (%s)\" % multi_class)\n    plt.axis(\"tight\")\n\n    # Plot also the training points\n    colors = \"bry\"\n    for i, color in zip(clf.classes_, colors):\n        idx = np.where(y == i)\n        plt.scatter(X[idx, 0], X[idx, 1], c=color, edgecolor=\"black\", s=20)\n\n    # Plot the three one-against-all classifiers\n    xmin, xmax = plt.xlim()\n    ymin, ymax = plt.ylim()\n    if multi_class == \"ovr\":\n        coef = np.concatenate([est.coef_ for est in clf.estimators_])\n        intercept = np.concatenate([est.intercept_ for est in clf.estimators_])\n    else:\n        coef = clf.coef_\n        intercept = clf.intercept_\n\n    def plot_hyperplane(c, color):\n        def line(x0):\n            return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]\n\n        plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls=\"--\", color=color)\n\n    for i, color in zip(clf.classes_, colors):\n        plot_hyperplane(i, color)\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/4c1663175b07cf9608b07331aa180eb7/plot_logistic_multinomial.ipynb

@@ -46,9 +46,7 @@
     colors = "bry"
     for i, color in zip(clf.classes_, colors):
         idx = np.where(y == i)
-        plt.scatter(
-            X[idx, 0], X[idx, 1], c=color, cmap=plt.cm.Paired, edgecolor="black", s=20
-        )
+        plt.scatter(X[idx, 0], X[idx, 1], c=color, edgecolor="black", s=20)
 
     # Plot the three one-against-all classifiers
     xmin, xmax = plt.xlim()