Pushing the docs to dev/ for branch: main, commit 8331fca667423c43af9598b03c475cbc29f025a8

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: 261086da5a179db4ef72d848b547be45
+config: e9be4bdfc77648c9c9817df4e93368ea
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -19,7 +19,9 @@
 
 """  # noqa: E501
 
+# %%
 import matplotlib
+import matplotlib.lines as mlines
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -44,8 +46,6 @@
 # Generate some abnormal novel observations
 X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
 
-xx, yy = np.meshgrid(np.linspace(-4.5, 4.5, 50), np.linspace(-4.5, 4.5, 50))
-
 # OCSVM hyperparameters
 nu = 0.05
 gamma = 2.0
@@ -60,10 +60,6 @@
 n_error_test = y_pred_test[y_pred_test == -1].size
 n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size
 
-Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
-Z = Z.reshape(xx.shape)
-
-
 # Fit the One-Class SVM using a kernel approximation and SGD
 transform = Nystroem(gamma=gamma, random_state=random_state)
 clf_sgd = SGDOneClassSVM(
@@ -78,25 +74,59 @@
 n_error_test_sgd = y_pred_test_sgd[y_pred_test_sgd == -1].size
 n_error_outliers_sgd = y_pred_outliers_sgd[y_pred_outliers_sgd == 1].size
 
-Z_sgd = pipe_sgd.decision_function(np.c_[xx.ravel(), yy.ravel()])
-Z_sgd = Z_sgd.reshape(xx.shape)
 
-# plot the level sets of the decision function
-plt.figure(figsize=(9, 6))
-plt.title("One Class SVM")
-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")
+# %%
+from sklearn.inspection import DecisionBoundaryDisplay
+
+_, ax = plt.subplots(figsize=(9, 6))
+
+xx, yy = np.meshgrid(np.linspace(-4.5, 4.5, 50), np.linspace(-4.5, 4.5, 50))
+X = np.concatenate([xx.ravel().reshape(-1, 1), yy.ravel().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="contour",
+    ax=ax,
+    linewidths=2,
+    colors="darkred",
+    levels=[0],
+)
+DecisionBoundaryDisplay.from_estimator(
+    clf,
+    X,
+    response_method="decision_function",
+    plot_method="contourf",
+    ax=ax,
+    colors="palevioletred",
+    levels=[0, clf.decision_function(X).max()],
+)
 
 s = 20
 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((-4.5, 4.5))
-plt.ylim((-4.5, 4.5))
-plt.legend(
-    [a.collections[0], b1, b2, c],
+
+ax.set(
+    title="One-Class SVM",
+    xlim=(-4.5, 4.5),
+    ylim=(-4.5, 4.5),
+    xlabel=(
+        f"error train: {n_error_train}/{X_train.shape[0]}; "
+        f"errors novel regular: {n_error_test}/{X_test.shape[0]}; "
+        f"errors novel abnormal: {n_error_outliers}/{X_outliers.shape[0]}"
+    ),
+)
+_ = ax.legend(
+    [mlines.Line2D([], [], color="darkred", label="learned frontier"), b1, b2, c],
     [
         "learned frontier",
         "training observations",
@@ -105,34 +135,57 @@
     ],
     loc="upper left",
 )
-plt.xlabel(
-    "error train: %d/%d; errors novel regular: %d/%d; errors novel abnormal: %d/%d"
-    % (
-        n_error_train,
-        X_train.shape[0],
-        n_error_test,
-        X_test.shape[0],
-        n_error_outliers,
-        X_outliers.shape[0],
-    )
-)
-plt.show()
 
-plt.figure(figsize=(9, 6))
-plt.title("Online One-Class SVM")
-plt.contourf(xx, yy, Z_sgd, levels=np.linspace(Z_sgd.min(), 0, 7), cmap=plt.cm.PuBu)
-a = plt.contour(xx, yy, Z_sgd, levels=[0], linewidths=2, colors="darkred")
-plt.contourf(xx, yy, Z_sgd, levels=[0, Z_sgd.max()], colors="palevioletred")
+# %%
+_, ax = plt.subplots(figsize=(9, 6))
+
+xx, yy = np.meshgrid(np.linspace(-4.5, 4.5, 50), np.linspace(-4.5, 4.5, 50))
+X = np.concatenate([xx.ravel().reshape(-1, 1), yy.ravel().reshape(-1, 1)], axis=1)
+DecisionBoundaryDisplay.from_estimator(
+    pipe_sgd,
+    X,
+    response_method="decision_function",
+    plot_method="contourf",
+    ax=ax,
+    cmap="PuBu",
+)
+DecisionBoundaryDisplay.from_estimator(
+    pipe_sgd,
+    X,
+    response_method="decision_function",
+    plot_method="contour",
+    ax=ax,
+    linewidths=2,
+    colors="darkred",
+    levels=[0],
+)
+DecisionBoundaryDisplay.from_estimator(
+    pipe_sgd,
+    X,
+    response_method="decision_function",
+    plot_method="contourf",
+    ax=ax,
+    colors="palevioletred",
+    levels=[0, pipe_sgd.decision_function(X).max()],
+)
 
 s = 20
 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((-4.5, 4.5))
-plt.ylim((-4.5, 4.5))
-plt.legend(
-    [a.collections[0], b1, b2, c],
+
+ax.set(
+    title="Online One-Class SVM",
+    xlim=(-4.5, 4.5),
+    ylim=(-4.5, 4.5),
+    xlabel=(
+        f"error train: {n_error_train_sgd}/{X_train.shape[0]}; "
+        f"errors novel regular: {n_error_test_sgd}/{X_test.shape[0]}; "
+        f"errors novel abnormal: {n_error_outliers_sgd}/{X_outliers.shape[0]}"
+    ),
+)
+ax.legend(
+    [mlines.Line2D([], [], color="darkred", label="learned frontier"), b1, b2, c],
     [
         "learned frontier",
         "training observations",
@@ -141,15 +194,4 @@
     ],
     loc="upper left",
 )
-plt.xlabel(
-    "error train: %d/%d; errors novel regular: %d/%d; errors novel abnormal: %d/%d"
-    % (
-        n_error_train_sgd,
-        X_train.shape[0],
-        n_error_test_sgd,
-        X_test.shape[0],
-        n_error_outliers_sgd,
-        X_outliers.shape[0],
-    )
-)
 plt.show()

dev/_downloads/3692f2475b52d40c3632818c00ff5b52/plot_sgdocsvm_vs_ocsvm.py

@@ -15,7 +15,29 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.linear_model import SGDOneClassSVM\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.svm import OneClassSVM\n\nfont = {\"weight\": \"normal\", \"size\": 15}\n\nmatplotlib.rc(\"font\", **font)\n\nrandom_state = 42\nrng = np.random.RandomState(random_state)\n\n# Generate train data\nX = 0.3 * rng.randn(500, 2)\nX_train = np.r_[X + 2, X - 2]\n# Generate some regular novel observations\nX = 0.3 * rng.randn(20, 2)\nX_test = np.r_[X + 2, X - 2]\n# Generate some abnormal novel observations\nX_outliers = rng.uniform(low=-4, high=4, size=(20, 2))\n\nxx, yy = np.meshgrid(np.linspace(-4.5, 4.5, 50), np.linspace(-4.5, 4.5, 50))\n\n# OCSVM hyperparameters\nnu = 0.05\ngamma = 2.0\n\n# Fit the One-Class SVM\nclf = OneClassSVM(gamma=gamma, kernel=\"rbf\", nu=nu)\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\nZ = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\nZ = Z.reshape(xx.shape)\n\n\n# Fit the One-Class SVM using a kernel approximation and SGD\ntransform = Nystroem(gamma=gamma, random_state=random_state)\nclf_sgd = SGDOneClassSVM(\n    nu=nu, shuffle=True, fit_intercept=True, random_state=random_state, tol=1e-4\n)\npipe_sgd = make_pipeline(transform, clf_sgd)\npipe_sgd.fit(X_train)\ny_pred_train_sgd = pipe_sgd.predict(X_train)\ny_pred_test_sgd = pipe_sgd.predict(X_test)\ny_pred_outliers_sgd = pipe_sgd.predict(X_outliers)\nn_error_train_sgd = y_pred_train_sgd[y_pred_train_sgd == -1].size\nn_error_test_sgd = y_pred_test_sgd[y_pred_test_sgd == -1].size\nn_error_outliers_sgd = y_pred_outliers_sgd[y_pred_outliers_sgd == 1].size\n\nZ_sgd = pipe_sgd.decision_function(np.c_[xx.ravel(), yy.ravel()])\nZ_sgd = Z_sgd.reshape(xx.shape)\n\n# plot the level sets of the decision function\nplt.figure(figsize=(9, 6))\nplt.title(\"One Class SVM\")\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 = 20\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((-4.5, 4.5))\nplt.ylim((-4.5, 4.5))\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)\nplt.xlabel(\n    \"error train: %d/%d; errors novel regular: %d/%d; errors novel abnormal: %d/%d\"\n    % (\n        n_error_train,\n        X_train.shape[0],\n        n_error_test,\n        X_test.shape[0],\n        n_error_outliers,\n        X_outliers.shape[0],\n    )\n)\nplt.show()\n\nplt.figure(figsize=(9, 6))\nplt.title(\"Online One-Class SVM\")\nplt.contourf(xx, yy, Z_sgd, levels=np.linspace(Z_sgd.min(), 0, 7), cmap=plt.cm.PuBu)\na = plt.contour(xx, yy, Z_sgd, levels=[0], linewidths=2, colors=\"darkred\")\nplt.contourf(xx, yy, Z_sgd, levels=[0, Z_sgd.max()], colors=\"palevioletred\")\n\ns = 20\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((-4.5, 4.5))\nplt.ylim((-4.5, 4.5))\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)\nplt.xlabel(\n    \"error train: %d/%d; errors novel regular: %d/%d; errors novel abnormal: %d/%d\"\n    % (\n        n_error_train_sgd,\n        X_train.shape[0],\n        n_error_test_sgd,\n        X_test.shape[0],\n        n_error_outliers_sgd,\n        X_outliers.shape[0],\n    )\n)\nplt.show()"
+        "import matplotlib\nimport matplotlib.lines as mlines\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.linear_model import SGDOneClassSVM\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.svm import OneClassSVM\n\nfont = {\"weight\": \"normal\", \"size\": 15}\n\nmatplotlib.rc(\"font\", **font)\n\nrandom_state = 42\nrng = np.random.RandomState(random_state)\n\n# Generate train data\nX = 0.3 * rng.randn(500, 2)\nX_train = np.r_[X + 2, X - 2]\n# Generate some regular novel observations\nX = 0.3 * rng.randn(20, 2)\nX_test = np.r_[X + 2, X - 2]\n# Generate some abnormal novel observations\nX_outliers = rng.uniform(low=-4, high=4, size=(20, 2))\n\n# OCSVM hyperparameters\nnu = 0.05\ngamma = 2.0\n\n# Fit the One-Class SVM\nclf = OneClassSVM(gamma=gamma, kernel=\"rbf\", nu=nu)\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# Fit the One-Class SVM using a kernel approximation and SGD\ntransform = Nystroem(gamma=gamma, random_state=random_state)\nclf_sgd = SGDOneClassSVM(\n    nu=nu, shuffle=True, fit_intercept=True, random_state=random_state, tol=1e-4\n)\npipe_sgd = make_pipeline(transform, clf_sgd)\npipe_sgd.fit(X_train)\ny_pred_train_sgd = pipe_sgd.predict(X_train)\ny_pred_test_sgd = pipe_sgd.predict(X_test)\ny_pred_outliers_sgd = pipe_sgd.predict(X_outliers)\nn_error_train_sgd = y_pred_train_sgd[y_pred_train_sgd == -1].size\nn_error_test_sgd = y_pred_test_sgd[y_pred_test_sgd == -1].size\nn_error_outliers_sgd = y_pred_outliers_sgd[y_pred_outliers_sgd == 1].size"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.inspection import DecisionBoundaryDisplay\n\n_, ax = plt.subplots(figsize=(9, 6))\n\nxx, yy = np.meshgrid(np.linspace(-4.5, 4.5, 50), np.linspace(-4.5, 4.5, 50))\nX = np.concatenate([xx.ravel().reshape(-1, 1), yy.ravel().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=\"contour\",\n    ax=ax,\n    linewidths=2,\n    colors=\"darkred\",\n    levels=[0],\n)\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    response_method=\"decision_function\",\n    plot_method=\"contourf\",\n    ax=ax,\n    colors=\"palevioletred\",\n    levels=[0, clf.decision_function(X).max()],\n)\n\ns = 20\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\")\n\nax.set(\n    title=\"One-Class SVM\",\n    xlim=(-4.5, 4.5),\n    ylim=(-4.5, 4.5),\n    xlabel=(\n        f\"error train: {n_error_train}/{X_train.shape[0]}; \"\n        f\"errors novel regular: {n_error_test}/{X_test.shape[0]}; \"\n        f\"errors novel abnormal: {n_error_outliers}/{X_outliers.shape[0]}\"\n    ),\n)\n_ = ax.legend(\n    [mlines.Line2D([], [], color=\"darkred\", label=\"learned frontier\"), b1, b2, c],\n    [\n        \"learned frontier\",\n        \"training observations\",\n        \"new regular observations\",\n        \"new abnormal observations\",\n    ],\n    loc=\"upper left\",\n)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "_, ax = plt.subplots(figsize=(9, 6))\n\nxx, yy = np.meshgrid(np.linspace(-4.5, 4.5, 50), np.linspace(-4.5, 4.5, 50))\nX = np.concatenate([xx.ravel().reshape(-1, 1), yy.ravel().reshape(-1, 1)], axis=1)\nDecisionBoundaryDisplay.from_estimator(\n    pipe_sgd,\n    X,\n    response_method=\"decision_function\",\n    plot_method=\"contourf\",\n    ax=ax,\n    cmap=\"PuBu\",\n)\nDecisionBoundaryDisplay.from_estimator(\n    pipe_sgd,\n    X,\n    response_method=\"decision_function\",\n    plot_method=\"contour\",\n    ax=ax,\n    linewidths=2,\n    colors=\"darkred\",\n    levels=[0],\n)\nDecisionBoundaryDisplay.from_estimator(\n    pipe_sgd,\n    X,\n    response_method=\"decision_function\",\n    plot_method=\"contourf\",\n    ax=ax,\n    colors=\"palevioletred\",\n    levels=[0, pipe_sgd.decision_function(X).max()],\n)\n\ns = 20\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\")\n\nax.set(\n    title=\"Online One-Class SVM\",\n    xlim=(-4.5, 4.5),\n    ylim=(-4.5, 4.5),\n    xlabel=(\n        f\"error train: {n_error_train_sgd}/{X_train.shape[0]}; \"\n        f\"errors novel regular: {n_error_test_sgd}/{X_test.shape[0]}; \"\n        f\"errors novel abnormal: {n_error_outliers_sgd}/{X_outliers.shape[0]}\"\n    ),\n)\nax.legend(\n    [mlines.Line2D([], [], color=\"darkred\", label=\"learned frontier\"), b1, b2, c],\n    [\n        \"learned frontier\",\n        \"training observations\",\n        \"new regular observations\",\n        \"new abnormal observations\",\n    ],\n    loc=\"upper left\",\n)\nplt.show()"
       ]
     }
   ],