Pushing the docs to dev/ for branch: main, commit 8798dfebd5767d4bcd6ef5f113c48df199204cfa

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.linear_model import SGDClassifier\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could\n# avoid this ugly slicing by using a two-dim dataset\nX = iris.data[:, :2]\ny = iris.target\ncolors = \"bry\"\n\n# shuffle\nidx = np.arange(X.shape[0])\nnp.random.seed(13)\nnp.random.shuffle(idx)\nX = X[idx]\ny = y[idx]\n\n# standardize\nmean = X.mean(axis=0)\nstd = X.std(axis=0)\nX = (X - mean) / std\n\nclf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)\nax = plt.gca()\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    cmap=plt.cm.Paired,\n    ax=ax,\n    response_method=\"predict\",\n    xlabel=iris.feature_names[0],\n    ylabel=iris.feature_names[1],\n)\nplt.axis(\"tight\")\n\n# Plot also the training points\nfor i, color in zip(clf.classes_, colors):\n    idx = np.where(y == i)\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=color,\n        label=iris.target_names[i],\n        cmap=plt.cm.Paired,\n        edgecolor=\"black\",\n        s=20,\n    )\nplt.title(\"Decision surface of multi-class SGD\")\nplt.axis(\"tight\")\n\n# Plot the three one-against-all classifiers\nxmin, xmax = plt.xlim()\nymin, ymax = plt.ylim()\ncoef = clf.coef_\nintercept = clf.intercept_\n\n\ndef 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\nfor i, color in zip(clf.classes_, colors):\n    plot_hyperplane(i, color)\nplt.legend()\nplt.show()"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.linear_model import SGDClassifier\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could\n# avoid this ugly slicing by using a two-dim dataset\nX = iris.data[:, :2]\ny = iris.target\ncolors = \"bry\"\n\n# shuffle\nidx = np.arange(X.shape[0])\nnp.random.seed(13)\nnp.random.shuffle(idx)\nX = X[idx]\ny = y[idx]\n\n# standardize\nmean = X.mean(axis=0)\nstd = X.std(axis=0)\nX = (X - mean) / std\n\nclf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)\nax = plt.gca()\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    cmap=plt.cm.Paired,\n    ax=ax,\n    response_method=\"predict\",\n    xlabel=iris.feature_names[0],\n    ylabel=iris.feature_names[1],\n)\nplt.axis(\"tight\")\n\n# Plot also the training points\nfor i, color in zip(clf.classes_, colors):\n    idx = np.where(y == i)\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=color,\n        label=iris.target_names[i],\n        edgecolor=\"black\",\n        s=20,\n    )\nplt.title(\"Decision surface of multi-class SGD\")\nplt.axis(\"tight\")\n\n# Plot the three one-against-all classifiers\nxmin, xmax = plt.xlim()\nymin, ymax = plt.ylim()\ncoef = clf.coef_\nintercept = clf.intercept_\n\n\ndef 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\nfor i, color in zip(clf.classes_, colors):\n    plot_hyperplane(i, color)\nplt.legend()\nplt.show()"
       ]
     }
   ],

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -58,7 +58,6 @@
         X[idx, 1],
         c=color,
         label=iris.target_names[i],
-        cmap=plt.cm.Paired,
         edgecolor="black",
         s=20,
     )