Anamoly Detection using Machine Learning

Jul 02, 2019

Identifying outliers using Isolation Forest

Anamolies in Data

Outlier detection is similar to novelty detection in the sense that the goal is to separate a core of regular observations from some polluting ones, called outliers. Yet, in the case of outlier detection, we don’t have a clean data set representing the population of regular observations that can be used to train any tool.

Isolation Forest

One efficient way of performing outlier detection in high-dimensional datasets is to use random forests. The ensemble.IsolationForest ‘isolates’ observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

Since recursive partitioning can be represented by a tree structure, the number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node.

This path length, averaged over a forest of such random trees, is a measure of normality and our decision function.

Random partioning produces noticeable shorter paths for anomalies. Hence, when a forest of random trees collectively produce shorter path lengths for particular samples, they are highly likely to be anomalies.


import numpy as np
import matplotlib.pyplot as plt

rng = np.random.RandomState(1082)
# Generate train data
X = 0.3 * rng.randn(100, 2)
X_train = np.r_[X+2, X-2]

# Generate some regular novel observations
X = 0.3 * rng.randn(20, 2)
X_test = np.r_[X+2, X-2]

# Generate some abnormal novel observations
X_outliers = rng.uniform(low=-4, high=4, size=(20,2))

# fit the model
from sklearn.ensemble import IsolationForest
clf = IsolationForest(behavior='new', max_samples=100, random_state=rng, contamination='auto')
y_pred_train = clf.predict(X_train)
y_pred_test = clf.predict(X_test)
y_pred_outlier = clf.predict(X_outliers)

# plot the line, the samples, and the nearest vectors to the plane
xx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

plt.contourf(xx, yy, Z,

b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white',
                 s=20, edgecolor='k')
b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='green',
                 s=20, edgecolor='k')
c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red',
                s=20, edgecolor='k')
plt.xlim((-5, 5))
plt.ylim((-5, 5))
plt.legend([b1, b2, c],
           ["training observations",
                       "new regular observations", "new abnormal observations"],
                                  loc="upper left")


Isolation Forest