Pushing the docs to dev/ for branch: main, commit 15504329657fc6efc11a68cf1b672fc7e2289420

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: 98f80686770ccc4ae1b4560577c24427
+config: 261086da5a179db4ef72d848b547be45
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,7 +15,18 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.font_manager\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import svm\n\nxx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))\n# Generate train data\nX = 0.3 * np.random.randn(100, 2)\nX_train = np.r_[X + 2, X - 2]\n# Generate some regular novel observations\nX = 0.3 * np.random.randn(20, 2)\nX_test = np.r_[X + 2, X - 2]\n# Generate some abnormal novel observations\nX_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))\n\n# fit the model\nclf = svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.1)\nclf.fit(X_train)\ny_pred_train = clf.predict(X_train)\ny_pred_test = clf.predict(X_test)\ny_pred_outliers = clf.predict(X_outliers)\nn_error_train = y_pred_train[y_pred_train == -1].size\nn_error_test = y_pred_test[y_pred_test == -1].size\nn_error_outliers = y_pred_outliers[y_pred_outliers == 1].size\n\n# plot the line, the points, and the nearest vectors to the plane\nZ = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\nZ = Z.reshape(xx.shape)\n\nplt.title(\"Novelty Detection\")\nplt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.PuBu)\na = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors=\"darkred\")\nplt.contourf(xx, yy, Z, levels=[0, Z.max()], colors=\"palevioletred\")\n\ns = 40\nb1 = plt.scatter(X_train[:, 0], X_train[:, 1], c=\"white\", s=s, edgecolors=\"k\")\nb2 = plt.scatter(X_test[:, 0], X_test[:, 1], c=\"blueviolet\", s=s, edgecolors=\"k\")\nc = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c=\"gold\", s=s, edgecolors=\"k\")\nplt.axis(\"tight\")\nplt.xlim((-5, 5))\nplt.ylim((-5, 5))\nplt.legend(\n    [a.collections[0], b1, b2, c],\n    [\n        \"learned frontier\",\n        \"training observations\",\n        \"new regular observations\",\n        \"new abnormal observations\",\n    ],\n    loc=\"upper left\",\n    prop=matplotlib.font_manager.FontProperties(size=11),\n)\nplt.xlabel(\n    \"error train: %d/200 ; errors novel regular: %d/40 ; errors novel abnormal: %d/40\"\n    % (n_error_train, n_error_test, n_error_outliers)\n)\nplt.show()"
+        "import numpy as np\n\nfrom sklearn import svm\n\n# Generate train data\nX = 0.3 * np.random.randn(100, 2)\nX_train = np.r_[X + 2, X - 2]\n# Generate some regular novel observations\nX = 0.3 * np.random.randn(20, 2)\nX_test = np.r_[X + 2, X - 2]\n# Generate some abnormal novel observations\nX_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))\n\n# fit the model\nclf = svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.1)\nclf.fit(X_train)\ny_pred_train = clf.predict(X_train)\ny_pred_test = clf.predict(X_test)\ny_pred_outliers = clf.predict(X_outliers)\nn_error_train = y_pred_train[y_pred_train == -1].size\nn_error_test = y_pred_test[y_pred_test == -1].size\nn_error_outliers = y_pred_outliers[y_pred_outliers == 1].size"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.font_manager\nimport matplotlib.lines as mlines\nimport matplotlib.pyplot as plt\n\nfrom sklearn.inspection import DecisionBoundaryDisplay\n\n_, ax = plt.subplots()\n\n# generate grid for the boundary display\nxx, yy = np.meshgrid(np.linspace(-5, 5, 10), np.linspace(-5, 5, 10))\nX = np.concatenate([xx.reshape(-1, 1), yy.reshape(-1, 1)], axis=1)\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    response_method=\"decision_function\",\n    plot_method=\"contourf\",\n    ax=ax,\n    cmap=\"PuBu\",\n)\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    response_method=\"decision_function\",\n    plot_method=\"contourf\",\n    ax=ax,\n    levels=[0, 10000],\n    colors=\"palevioletred\",\n)\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    response_method=\"decision_function\",\n    plot_method=\"contour\",\n    ax=ax,\n    levels=[0],\n    colors=\"darkred\",\n    linewidths=2,\n)\n\ns = 40\nb1 = ax.scatter(X_train[:, 0], X_train[:, 1], c=\"white\", s=s, edgecolors=\"k\")\nb2 = ax.scatter(X_test[:, 0], X_test[:, 1], c=\"blueviolet\", s=s, edgecolors=\"k\")\nc = ax.scatter(X_outliers[:, 0], X_outliers[:, 1], c=\"gold\", s=s, edgecolors=\"k\")\nplt.legend(\n    [mlines.Line2D([], [], color=\"darkred\"), b1, b2, c],\n    [\n        \"learned frontier\",\n        \"training observations\",\n        \"new regular observations\",\n        \"new abnormal observations\",\n    ],\n    loc=\"upper left\",\n    prop=matplotlib.font_manager.FontProperties(size=11),\n)\nax.set(\n    xlabel=(\n        f\"error train: {n_error_train}/200 ; errors novel regular: {n_error_test}/40 ;\"\n        f\" errors novel abnormal: {n_error_outliers}/40\"\n    ),\n    title=\"Novelty Detection\",\n    xlim=(-5, 5),\n    ylim=(-5, 5),\n)\nplt.show()"
       ]
     }
   ],

dev/_downloads/179a84f8da8ce09af733c9a82135ca4d/plot_oneclass.ipynb

@@ -11,13 +11,11 @@
 
 """
 
-import matplotlib.font_manager
-import matplotlib.pyplot as plt
+# %%
 import numpy as np
 
 from sklearn import svm
 
-xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))
 # Generate train data
 X = 0.3 * np.random.randn(100, 2)
 X_train = np.r_[X + 2, X - 2]
@@ -37,24 +35,52 @@
 n_error_test = y_pred_test[y_pred_test == -1].size
 n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size
 
-# plot the line, the points, and the nearest vectors to the plane
-Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
-Z = Z.reshape(xx.shape)
+# %%
+import matplotlib.font_manager
+import matplotlib.lines as mlines
+import matplotlib.pyplot as plt
+
+from sklearn.inspection import DecisionBoundaryDisplay
 
-plt.title("Novelty Detection")
-plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.PuBu)
-a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors="darkred")
-plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors="palevioletred")
+_, ax = plt.subplots()
+
+# generate grid for the boundary display
+xx, yy = np.meshgrid(np.linspace(-5, 5, 10), np.linspace(-5, 5, 10))
+X = np.concatenate([xx.reshape(-1, 1), yy.reshape(-1, 1)], axis=1)
+DecisionBoundaryDisplay.from_estimator(
+    clf,
+    X,
+    response_method="decision_function",
+    plot_method="contourf",
+    ax=ax,
+    cmap="PuBu",
+)
+DecisionBoundaryDisplay.from_estimator(
+    clf,
+    X,
+    response_method="decision_function",
+    plot_method="contourf",
+    ax=ax,
+    levels=[0, 10000],
+    colors="palevioletred",
+)
+DecisionBoundaryDisplay.from_estimator(
+    clf,
+    X,
+    response_method="decision_function",
+    plot_method="contour",
+    ax=ax,
+    levels=[0],
+    colors="darkred",
+    linewidths=2,
+)
 
 s = 40
-b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c="white", s=s, edgecolors="k")
-b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c="blueviolet", s=s, edgecolors="k")
-c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c="gold", s=s, edgecolors="k")
-plt.axis("tight")
-plt.xlim((-5, 5))
-plt.ylim((-5, 5))
+b1 = ax.scatter(X_train[:, 0], X_train[:, 1], c="white", s=s, edgecolors="k")
+b2 = ax.scatter(X_test[:, 0], X_test[:, 1], c="blueviolet", s=s, edgecolors="k")
+c = ax.scatter(X_outliers[:, 0], X_outliers[:, 1], c="gold", s=s, edgecolors="k")
 plt.legend(
-    [a.collections[0], b1, b2, c],
+    [mlines.Line2D([], [], color="darkred"), b1, b2, c],
     [
         "learned frontier",
         "training observations",
@@ -64,8 +90,13 @@
     loc="upper left",
     prop=matplotlib.font_manager.FontProperties(size=11),
 )
-plt.xlabel(
-    "error train: %d/200 ; errors novel regular: %d/40 ; errors novel abnormal: %d/40"
-    % (n_error_train, n_error_test, n_error_outliers)
+ax.set(
+    xlabel=(
+        f"error train: {n_error_train}/200 ; errors novel regular: {n_error_test}/40 ;"
+        f" errors novel abnormal: {n_error_outliers}/40"
+    ),
+    title="Novelty Detection",
+    xlim=(-5, 5),
+    ylim=(-5, 5),
 )
 plt.show()