.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/neighbors/plot_lof_novelty_detection.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_neighbors_plot_lof_novelty_detection.py: ================================================= Novelty detection with Local Outlier Factor (LOF) ================================================= The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection method which computes the local density deviation of a given data point with respect to its neighbors. It considers as outliers the samples that have a substantially lower density than their neighbors. This example shows how to use LOF for novelty detection. Note that when LOF is used for novelty detection you MUST not use predict, decision_function and score_samples on the training set as this would lead to wrong results. You must only use these methods on new unseen data (which are not in the training set). See :ref:`User Guide `: for details on the difference between outlier detection and novelty detection and how to use LOF for outlier detection. The number of neighbors considered, (parameter n_neighbors) is typically set 1) greater than the minimum number of samples a cluster has to contain, so that other samples can be local outliers relative to this cluster, and 2) smaller than the maximum number of close by samples that can potentially be local outliers. In practice, such information is generally not available, and taking n_neighbors=20 appears to work well in general. .. GENERATED FROM PYTHON SOURCE LINES 27-93 .. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_lof_novelty_detection_001.png :alt: Novelty Detection with LOF :srcset: /auto_examples/neighbors/images/sphx_glr_plot_lof_novelty_detection_001.png :class: sphx-glr-single-img .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import matplotlib import matplotlib.lines as mlines import matplotlib.pyplot as plt import numpy as np from sklearn.neighbors import LocalOutlierFactor np.random.seed(42) xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500)) # Generate normal (not abnormal) training observations X = 0.3 * np.random.randn(100, 2) X_train = np.r_[X + 2, X - 2] # Generate new normal (not abnormal) 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 for novelty detection (novelty=True) clf = LocalOutlierFactor(n_neighbors=20, novelty=True, contamination=0.1) clf.fit(X_train) # DO NOT use predict, decision_function and score_samples on X_train as this # would give wrong results but only on new unseen data (not used in X_train), # e.g. X_test, X_outliers or the meshgrid y_pred_test = clf.predict(X_test) y_pred_outliers = clf.predict(X_outliers) n_error_test = y_pred_test[y_pred_test == -1].size n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size # plot the learned frontier, 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 with LOF") 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( [mlines.Line2D([], [], color="darkred"), 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( "errors novel regular: %d/40 ; errors novel abnormal: %d/40" % (n_error_test, n_error_outliers) ) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.727 seconds) .. _sphx_glr_download_auto_examples_neighbors_plot_lof_novelty_detection.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.6.X?urlpath=lab/tree/notebooks/auto_examples/neighbors/plot_lof_novelty_detection.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/neighbors/plot_lof_novelty_detection.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_lof_novelty_detection.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_lof_novelty_detection.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_lof_novelty_detection.zip ` .. include:: plot_lof_novelty_detection.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_