dev/lite/files/auto_examples/svm/plot_oneclass.ipynb

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        "<div class='alert alert-warning'>\n\n# JupyterLite warning\n\nRunning the scikit-learn examples in JupyterLite is experimental and you may encounter some unexpected behavior.\n\nThe main difference is that imports will take a lot longer than usual, for example the first `import sklearn` can take roughly 10-20s.\n\nIf you notice problems, feel free to open an [issue](https://github.com/scikit-learn/scikit-learn/issues/new/choose) about it.\n</div>"
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        "# JupyterLite-specific code\nimport matplotlib\nimport pandas"
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      "source": [
        "\n# One-class SVM with non-linear kernel (RBF)\n\nAn example using a one-class SVM for novelty detection.\n\n`One-class SVM <svm_outlier_detection>` is an unsupervised\nalgorithm that learns a decision function for novelty detection:\nclassifying new data as similar or different to the training set.\n"
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      "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()"
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