Comparing anomaly detection algorithms for outlier detection on toy datasets¶
This example shows characteristics of different anomaly detection algorithms on 2D datasets. Datasets contain one or two modes (regions of high density) to illustrate the ability of algorithms to cope with multimodal data.
For each dataset, 15% of samples are generated as random uniform noise. This proportion is the value given to the nu parameter of the OneClassSVM and the contamination parameter of the other outlier detection algorithms. Decision boundaries between inliers and outliers are displayed in black except for Local Outlier Factor (LOF) as it has no predict method to be applied on new data when it is used for outlier detection.
sklearn.svm.OneClassSVM is known to be sensitive to outliers and
thus does not perform very well for outlier detection. This estimator is best
suited for novelty detection when the training set is not contaminated by
outliers. That said, outlier detection in high-dimension, or without any
assumptions on the distribution of the inlying data is very challenging, and a
One-class SVM might give useful results in these situations depending on the
value of its hyperparameters.
sklearn.covariance.EllipticEnvelope assumes the data is Gaussian and
learns an ellipse. It thus degrades when the data is not unimodal. Notice
however that this estimator is robust to outliers.
sklearn.neighbors.LocalOutlierFactor seem to perform reasonably well
for multi-modal data sets. The advantage of
sklearn.neighbors.LocalOutlierFactor over the other estimators is
shown for the third data set, where the two modes have different densities.
This advantage is explained by the local aspect of LOF, meaning that it only
compares the score of abnormality of one sample with the scores of its
Finally, for the last data set, it is hard to say that one sample is more
abnormal than another sample as they are uniformly distributed in a
hypercube. Except for the
sklearn.svm.OneClassSVM which overfits a
little, all estimators present decent solutions for this situation. In such a
case, it would be wise to look more closely at the scores of abnormality of
the samples as a good estimator should assign similar scores to all the
While these examples give some intuition about the algorithms, this intuition might not apply to very high dimensional data.
Finally, note that parameters of the models have been here handpicked but that in practice they need to be adjusted. In the absence of labelled data, the problem is completely unsupervised so model selection can be a challenge.
# Author: Alexandre Gramfort <firstname.lastname@example.org> # Albert Thomas <email@example.com> # License: BSD 3 clause import time import numpy as np import matplotlib import matplotlib.pyplot as plt from sklearn import svm from sklearn.datasets import make_moons, make_blobs from sklearn.covariance import EllipticEnvelope from sklearn.ensemble import IsolationForest from sklearn.neighbors import LocalOutlierFactor print(__doc__) matplotlib.rcParams['contour.negative_linestyle'] = 'solid' # Example settings n_samples = 300 outliers_fraction = 0.15 n_outliers = int(outliers_fraction * n_samples) n_inliers = n_samples - n_outliers # define outlier/anomaly detection methods to be compared anomaly_algorithms = [ ("Robust covariance", EllipticEnvelope(contamination=outliers_fraction)), ("One-Class SVM", svm.OneClassSVM(nu=outliers_fraction, kernel="rbf", gamma=0.1)), ("Isolation Forest", IsolationForest(contamination=outliers_fraction, random_state=42)), ("Local Outlier Factor", LocalOutlierFactor( n_neighbors=35, contamination=outliers_fraction))] # Define datasets blobs_params = dict(random_state=0, n_samples=n_inliers, n_features=2) datasets = [ make_blobs(centers=[[0, 0], [0, 0]], cluster_std=0.5, **blobs_params), make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[0.5, 0.5], **blobs_params), make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[1.5, .3], **blobs_params), 4. * (make_moons(n_samples=n_samples, noise=.05, random_state=0) - np.array([0.5, 0.25])), 14. * (np.random.RandomState(42).rand(n_samples, 2) - 0.5)] # Compare given classifiers under given settings xx, yy = np.meshgrid(np.linspace(-7, 7, 150), np.linspace(-7, 7, 150)) plt.figure(figsize=(len(anomaly_algorithms) * 2 + 3, 12.5)) plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05, hspace=.01) plot_num = 1 rng = np.random.RandomState(42) for i_dataset, X in enumerate(datasets): # Add outliers X = np.concatenate([X, rng.uniform(low=-6, high=6, size=(n_outliers, 2))], axis=0) for name, algorithm in anomaly_algorithms: t0 = time.time() algorithm.fit(X) t1 = time.time() plt.subplot(len(datasets), len(anomaly_algorithms), plot_num) if i_dataset == 0: plt.title(name, size=18) # fit the data and tag outliers if name == "Local Outlier Factor": y_pred = algorithm.fit_predict(X) else: y_pred = algorithm.fit(X).predict(X) # plot the levels lines and the points if name != "Local Outlier Factor": # LOF does not implement predict Z = algorithm.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) plt.contour(xx, yy, Z, levels=, linewidths=2, colors='black') colors = np.array(['#377eb8', '#ff7f00']) plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[(y_pred + 1) // 2]) plt.xlim(-7, 7) plt.ylim(-7, 7) plt.xticks(()) plt.yticks(()) plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'), transform=plt.gca().transAxes, size=15, horizontalalignment='right') plot_num += 1 plt.show()
Total running time of the script: ( 0 minutes 4.500 seconds)
Estimated memory usage: 8 MB