Anomaly/Novelty detection with scikit-learn
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This document discusses anomaly detection techniques in scikit-learn. It begins by defining anomalies and outliers, then describes the main types of anomaly detection as supervised, semi-supervised (novelty detection), and unsupervised. Popular density-based, kernel, nearest neighbors, and tree/partitioning approaches are covered. Examples are given using Gaussian mixture models, one-class SVM, local outlier factor, and isolation forest algorithms. Challenges in anomaly detection like parameter tuning and evaluation are also noted.
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Anomaly/Novelty detection with scikit-learn
- 1. Anomaly/Novelty Detection with scikit-learn Alexandre Gramfort Telecom ParisTech - CNRS LTCI alexandre.gramfort@telecom-paristech.fr GitHub : @agramfort Twitter : @agramfort
- 2. Alexandre Gramfort Anomaly detection with scikit-learn What’s the problem? 2 Objective: Spot the red apple
- 3. Alexandre Gramfort Anomaly detection with scikit-learn What’s the problem? 3 “An outlier is an observation in a data set which appears to be inconsistent with the remainder of that set of data.” Johnson 1992 “An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism.” Hawkins 1980 Outlier/Anomaly
- 4. Alexandre Gramfort Anomaly detection with scikit-learn Types of AD 4 • Supervised AD • Labels available for both normal data and anomalies • Similar to rare class mining / imbalanced classification • Semi-supervised AD (Novelty Detection) • Only normal data available to train • The algorithm learns on normal data only • Unsupervised AD (Outlier Detection) • no labels, training set = normal + abnormal data • Assumption: anomalies are very rare
- 5. Alexandre Gramfort Anomaly detection with scikit-learn Types of AD 5 • Supervised AD • Labels available for both normal data and anomalies • Similar to rare class mining / imbalanced classification • Semi-supervised AD (Novelty Detection) • Only normal data available to train • The algorithm learns on normal data only • Unsupervised AD (Outlier Detection) • no labels, training set = normal + abnormal data • Assumption: anomalies are very rare
- 6. Alexandre Gramfort Anomaly detection with scikit-learn ML Taxonomy 6 Machine Learning SupervisedUnsupervised Regression Classif. “Prediction” Clustering Dim. Red. Anomaly Novelty Detection
- 7. Alexandre Gramfort Anomaly detection with scikit-learn Applications 7 • Fraud detection • Network intrusion • Finance • Insurance • Maintenance • Medicine (unusual symptoms) • Measurement errors (from sensors) Any application where looking at unusual observations is relevant
- 9. Alexandre Gramfort Anomaly detection with scikit-learn Big picture 9 Look for samples that are in low density regions, isolated Look for a region of the space that is small in volume but contains most of the samples
- 10. Density based approach (KDE, Gaussian Ellipse, GMM) Kernel methods Nearest neighbors Trees / Partitioning
- 11. Novelty detection via density estimates >>> from sklearn.mixture import GaussianMixture >>> gmm = GaussianMixture(n_components=1).fit(X) >>> log_dens = gmm.score_samples(X_plot) >>> plt.fill(X_plot[:, 0], np.exp(log_dens), fc='#ffaf00', alpha=0.7) Low density regions
- 12. Novelty detection via density estimates >>> from sklearn.mixture import GaussianMixture >>> gmm = GaussianMixture(n_components=2).fit(X) >>> log_dens = gmm.score_samples(X_plot) >>> plt.fill(X_plot[:, 0], np.exp(log_dens), fc='#ffaf00', alpha=0.7)
- 13. Novelty detection via density estimates >>> from sklearn.neighbors import KernelDensity >>> kde = KernelDensity(kernel='gaussian', bandwidth=0.75).fit(X) >>> log_dens = kde.score_samples(X_plot) >>> plt.fill(X_plot[:, 0], np.exp(log_dens), fc='#ffaf00', alpha=0.7)
- 14. Kernel methods Nearest neighbors Trees / Partitioning
- 15. Our toy dataset
- 16. Kernel Approach http://scikit-learn.org/stable/auto_examples/svm/plot_oneclass.html >>> est = OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1)
- 17. Nearest neighbors Trees / Partitioning
- 18. Nearest Neighbors (NN) Approach https://github.com/scikit-learn/scikit-learn/pull/5279 >>> est = LocalOutlierFactor(n_neighbors=5)
- 19. Local Outlier Factor (LOF) https://en.wikipedia.org/wiki/Local_outlier_factor
- 21. Partitioning / Tree Approach http://scikit-learn.org/dev/modules/generated/sklearn.ensemble.IsolationForest.html >>> est = IsolationForest(n_estimators=100)
- 22. Isolation Forest An anomaly can be isolated with a very shallow random tree
- 25. Alexandre Gramfort Anomaly detection with scikit-learn Some caveats 25 • How to set model hyperparameters? • How to evaluate performance in the unsupervised setup? • In any AD method there is a notion of metric/similarity between samples, e.g. Euclidian distance. Unclear how to define it (think continuous, categorical features etc.)
- 28. Alexandre Gramfort alexandre.gramfort@telecom-paristech.frContact: GitHub : @agramfort Twitter : @agramfort Questions? 1 position to work on Scikit-Learn and Scipy stack available ! Thanks @ngoix & @albertthomas88 for the work