scikit-learn · jnothman · Nov 2, 2017 · Oct 25, 2017 · Oct 26, 2017 · Oct 26, 2017
diff --git a/doc/modules/outlier_detection.rst b/doc/modules/outlier_detection.rst
@@ -33,6 +33,17 @@ new observations can then be sorted as inliers or outliers with a
 
 Inliers are labeled 1, while outliers are labeled -1.
 
+Overview of outlier detection methods
+=====================================
+
+.. figure:: ../auto_examples/images/sphx_glr_plot_anomaly_comparison_001.png
+   :target: ../auto_examples/plot_anomaly_comparison.html
+   :align: center
+   :scale: 50
+
+   A comparison of the outlier detection algorithms in scikit-learn
+
+
 Novelty Detection
 =================
 

diff --git a/examples/plot_anomaly_comparison.py b/examples/plot_anomaly_comparison.py
@@ -0,0 +1,121 @@
+"""
+============================================================================
+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.
+
+Local Outlier Factor (LOF) does not show a decision boundary in black as it
+has no predict method to be applied on new data.
+
+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 <alexandre.gramfort@inria.fr>
+#         Albert Thomas <albert.thomas@telecom-paristech.fr>
+# 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)[0],
+    make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[1.5, .3],
+               **blobs_params)[0],
+    4. * (make_moons(n_samples=n_samples, noise=.05, random_state=0)[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=[0], 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()