.. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_svm_plot_oneclass.py: ========================================== One-class SVM with non-linear kernel (RBF) ========================================== An example using a one-class SVM for novelty detection. :ref:`One-class SVM ` is an unsupervised algorithm that learns a decision function for novelty detection: classifying new data as similar or different to the training set. .. image:: /auto_examples/svm/images/sphx_glr_plot_oneclass_001.png :alt: Novelty Detection :class: sphx-glr-single-img .. code-block:: default print(__doc__) import numpy as np import matplotlib.pyplot as plt import matplotlib.font_manager 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] # Generate some regular novel observations X = 0.3 * np.random.randn(20, 2) X_test = np.r_[X + 2, X - 2] # Generate some abnormal novel observations X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2)) # fit the model clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1) clf.fit(X_train) y_pred_train = clf.predict(X_train) y_pred_test = clf.predict(X_test) y_pred_outliers = clf.predict(X_outliers) n_error_train = y_pred_train[y_pred_train == -1].size 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) 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') 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)) plt.legend([a.collections[0], b1, b2, c], ["learned frontier", "training observations", "new regular observations", "new abnormal observations"], 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)) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.270 seconds) .. _sphx_glr_download_auto_examples_svm_plot_oneclass.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/svm/plot_oneclass.ipynb :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_oneclass.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_oneclass.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_