Pushing the docs to dev/ for branch: main, commit e31ba777bc5e66b04dcb58b2bdf95ff8a9d3741b

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: 68c657e4b58a45a23ad28d523944e85a
+config: 6bd6b226cfe6702d2bb6a77ccc4183ee
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.neighbors import LocalOutlierFactor\n\nnp.random.seed(42)\n\nxx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))\n# Generate normal (not abnormal) training observations\nX = 0.3 * np.random.randn(100, 2)\nX_train = np.r_[X + 2, X - 2]\n# Generate new normal (not abnormal) 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 for novelty detection (novelty=True)\nclf = LocalOutlierFactor(n_neighbors=20, novelty=True, contamination=0.1)\nclf.fit(X_train)\n# DO NOT use predict, decision_function and score_samples on X_train as this\n# would give wrong results but only on new unseen data (not used in X_train),\n# e.g. X_test, X_outliers or the meshgrid\ny_pred_test = clf.predict(X_test)\ny_pred_outliers = clf.predict(X_outliers)\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 learned frontier, 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 with LOF\")\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    \"errors novel regular: %d/40 ; errors novel abnormal: %d/40\"\n    % (n_error_test, n_error_outliers)\n)\nplt.show()"
+        "import matplotlib\nimport matplotlib.lines as mlines\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.neighbors import LocalOutlierFactor\n\nnp.random.seed(42)\n\nxx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))\n# Generate normal (not abnormal) training observations\nX = 0.3 * np.random.randn(100, 2)\nX_train = np.r_[X + 2, X - 2]\n# Generate new normal (not abnormal) 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 for novelty detection (novelty=True)\nclf = LocalOutlierFactor(n_neighbors=20, novelty=True, contamination=0.1)\nclf.fit(X_train)\n# DO NOT use predict, decision_function and score_samples on X_train as this\n# would give wrong results but only on new unseen data (not used in X_train),\n# e.g. X_test, X_outliers or the meshgrid\ny_pred_test = clf.predict(X_test)\ny_pred_outliers = clf.predict(X_outliers)\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 learned frontier, 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 with LOF\")\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    [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)\nplt.xlabel(\n    \"errors novel regular: %d/40 ; errors novel abnormal: %d/40\"\n    % (n_error_test, n_error_outliers)\n)\nplt.show()"
       ]
     }
   ],

dev/_downloads/1ecb1284f0b785cd0011e155bf44657c/plot_lof_novelty_detection.ipynb

@@ -26,6 +26,7 @@
 """
 
 import matplotlib
+import matplotlib.lines as mlines
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -71,7 +72,7 @@
 plt.xlim((-5, 5))
 plt.ylim((-5, 5))
 plt.legend(
-    [a.collections[0], b1, b2, c],
+    [mlines.Line2D([], [], color="darkred"), b1, b2, c],
     [
         "learned frontier",
         "training observations",