Pushing the docs to dev/ for branch: main, commit 36375c3ebf6438a46949c45bc8b3d8ecdcb19301

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Noel Dawe <[email protected]>\n#\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_gaussian_quantiles\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Construct dataset\nX1, y1 = make_gaussian_quantiles(\n    cov=2.0, n_samples=200, n_features=2, n_classes=2, random_state=1\n)\nX2, y2 = make_gaussian_quantiles(\n    mean=(3, 3), cov=1.5, n_samples=300, n_features=2, n_classes=2, random_state=1\n)\nX = np.concatenate((X1, X2))\ny = np.concatenate((y1, -y2 + 1))\n\n# Create and fit an AdaBoosted decision tree\nbdt = AdaBoostClassifier(\n    DecisionTreeClassifier(max_depth=1), algorithm=\"SAMME\", n_estimators=200\n)\n\nbdt.fit(X, y)\n\nplot_colors = \"br\"\nplot_step = 0.02\nclass_names = \"AB\"\n\nplt.figure(figsize=(10, 5))\n\n# Plot the decision boundaries\nax = plt.subplot(121)\ndisp = DecisionBoundaryDisplay.from_estimator(\n    bdt,\n    X,\n    cmap=plt.cm.Paired,\n    response_method=\"predict\",\n    ax=ax,\n    xlabel=\"x\",\n    ylabel=\"y\",\n)\nx_min, x_max = disp.xx0.min(), disp.xx0.max()\ny_min, y_max = disp.xx1.min(), disp.xx1.max()\nplt.axis(\"tight\")\n\n# Plot the training points\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    idx = np.where(y == i)\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=c,\n        cmap=plt.cm.Paired,\n        s=20,\n        edgecolor=\"k\",\n        label=\"Class %s\" % n,\n    )\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.legend(loc=\"upper right\")\n\nplt.title(\"Decision Boundary\")\n\n# Plot the two-class decision scores\ntwoclass_output = bdt.decision_function(X)\nplot_range = (twoclass_output.min(), twoclass_output.max())\nplt.subplot(122)\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    plt.hist(\n        twoclass_output[y == i],\n        bins=10,\n        range=plot_range,\n        facecolor=c,\n        label=\"Class %s\" % n,\n        alpha=0.5,\n        edgecolor=\"k\",\n    )\nx1, x2, y1, y2 = plt.axis()\nplt.axis((x1, x2, y1, y2 * 1.2))\nplt.legend(loc=\"upper right\")\nplt.ylabel(\"Samples\")\nplt.xlabel(\"Score\")\nplt.title(\"Decision Scores\")\n\nplt.tight_layout()\nplt.subplots_adjust(wspace=0.35)\nplt.show()"
+        "# Author: Noel Dawe <[email protected]>\n#\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_gaussian_quantiles\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Construct dataset\nX1, y1 = make_gaussian_quantiles(\n    cov=2.0, n_samples=200, n_features=2, n_classes=2, random_state=1\n)\nX2, y2 = make_gaussian_quantiles(\n    mean=(3, 3), cov=1.5, n_samples=300, n_features=2, n_classes=2, random_state=1\n)\nX = np.concatenate((X1, X2))\ny = np.concatenate((y1, -y2 + 1))\n\n# Create and fit an AdaBoosted decision tree\nbdt = AdaBoostClassifier(\n    DecisionTreeClassifier(max_depth=1), algorithm=\"SAMME\", n_estimators=200\n)\n\nbdt.fit(X, y)\n\nplot_colors = \"br\"\nplot_step = 0.02\nclass_names = \"AB\"\n\nplt.figure(figsize=(10, 5))\n\n# Plot the decision boundaries\nax = plt.subplot(121)\ndisp = DecisionBoundaryDisplay.from_estimator(\n    bdt,\n    X,\n    cmap=plt.cm.Paired,\n    response_method=\"predict\",\n    ax=ax,\n    xlabel=\"x\",\n    ylabel=\"y\",\n)\nx_min, x_max = disp.xx0.min(), disp.xx0.max()\ny_min, y_max = disp.xx1.min(), disp.xx1.max()\nplt.axis(\"tight\")\n\n# Plot the training points\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    idx = np.where(y == i)\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=c,\n        s=20,\n        edgecolor=\"k\",\n        label=\"Class %s\" % n,\n    )\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.legend(loc=\"upper right\")\n\nplt.title(\"Decision Boundary\")\n\n# Plot the two-class decision scores\ntwoclass_output = bdt.decision_function(X)\nplot_range = (twoclass_output.min(), twoclass_output.max())\nplt.subplot(122)\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    plt.hist(\n        twoclass_output[y == i],\n        bins=10,\n        range=plot_range,\n        facecolor=c,\n        label=\"Class %s\" % n,\n        alpha=0.5,\n        edgecolor=\"k\",\n    )\nx1, x2, y1, y2 = plt.axis()\nplt.axis((x1, x2, y1, y2 * 1.2))\nplt.legend(loc=\"upper right\")\nplt.ylabel(\"Samples\")\nplt.xlabel(\"Score\")\nplt.title(\"Decision Scores\")\n\nplt.tight_layout()\nplt.subplots_adjust(wspace=0.35)\nplt.show()"
       ]
     }
   ],

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -74,7 +74,6 @@
         X[idx, 0],
         X[idx, 1],
         c=c,
-        cmap=plt.cm.Paired,
         s=20,
         edgecolor="k",
         label="Class %s" % n,