Machine Learning for Anomaly Detection

Last Updated : 03 May, 2023

Anomaly Detection is the technique of identifying rare events or observations which can raise suspicions by being statistically different from the rest of the observations. Such "anomalous" behaviour typically translates to some kind of a problem like a credit card fraud, failing machine in a server, a cyber attack, etc.
An anomaly can be broadly categorized into three categories -

Point Anomaly: A tuple in a dataset is said to be a Point Anomaly if it is far off from the rest of the data.
Contextual Anomaly: An observation is a Contextual Anomaly if it is an anomaly because of the context of the observation.
Collective Anomaly: A set of data instances help in finding an anomaly.

Anomaly detection can be done using the concepts of Machine Learning. It can be done in the following ways -

Supervised Anomaly Detection: This method requires a labeled dataset containing both normal and anomalous samples to construct a predictive model to classify future data points. The most commonly used algorithms for this purpose are supervised Neural Networks, Support Vector Machine learning, K-Nearest Neighbors Classifier, etc.
Unsupervised Anomaly Detection: This method does require any training data and instead assumes two things about the data ie Only a small percentage of data is anomalous and Any anomaly is statistically different from the normal samples. Based on the above assumptions, the data is then clustered using a similarity measure and the data points which are far off from the cluster are considered to be anomalies.

We now demonstrate the process of anomaly detection on a synthetic dataset using the K-Nearest Neighbors algorithm which is included in the pyod module.
Step 1: Importing the required libraries

Python3

import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import matplotlib.font_manager
from pyod.models.knn import KNN 
from pyod.utils.data import generate_data, get_outliers_inliers

Step 2: Creating the synthetic data

Python3

# generating a random dataset with two features
X_train, y_train = generate_data(n_train = 300, train_only = True,
                                                   n_features = 2)

# Setting the percentage of outliers
outlier_fraction = 0.1

# Storing the outliers and inliners in different numpy arrays
X_outliers, X_inliers = get_outliers_inliers(X_train, y_train)
n_inliers = len(X_inliers)
n_outliers = len(X_outliers)

# Separating the two features
f1 = X_train[:, [0]].reshape(-1, 1)
f2 = X_train[:, [1]].reshape(-1, 1)

Step 3: Visualising the data

Python3

# Visualising the dataset
# create a meshgrid
xx, yy = np.meshgrid(np.linspace(-10, 10, 200),
                     np.linspace(-10, 10, 200))

# scatter plot
plt.scatter(f1, f2)
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')

Step 4: Training and evaluating the model

Python3

# Training the classifier
clf = KNN(contamination = outlier_fraction)
clf.fit(X_train, y_train)

# You can print this to see all the prediction scores
scores_pred = clf.decision_function(X_train)*-1

y_pred = clf.predict(X_train)
n_errors = (y_pred != y_train).sum()
# Counting the number of errors

print('The number of prediction errors are ' + str(n_errors))

Step 5: Visualising the predictions

Python3

# threshold value to consider a
# datapoint inlier or outlier
threshold = stats.scoreatpercentile(scores_pred, 100 * outlier_fraction)

# decision function calculates the raw 
# anomaly score for every point
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) * -1
Z = Z.reshape(xx.shape)

# fill blue colormap from minimum anomaly
# score to threshold value
subplot = plt.subplot(1, 2, 1)
subplot.contourf(xx, yy, Z, levels = np.linspace(Z.min(), 
                  threshold, 10), cmap = plt.cm.Blues_r)

# draw red contour line where anomaly 
# score is equal to threshold
a = subplot.contour(xx, yy, Z, levels =[threshold],
                     linewidths = 2, colors ='red')

# fill orange contour lines where range of anomaly
# score is from threshold to maximum anomaly score
subplot.contourf(xx, yy, Z, levels =[threshold, Z.max()], colors ='orange')

# scatter plot of inliers with white dots
b = subplot.scatter(X_train[:-n_outliers, 0], X_train[:-n_outliers, 1],
                                    c ='white', s = 20, edgecolor ='k') 

# scatter plot of outliers with black dots
c = subplot.scatter(X_train[-n_outliers:, 0], X_train[-n_outliers:, 1], 
                                    c ='black', s = 20, edgecolor ='k')
subplot.axis('tight')

subplot.legend(
    [a.collections[0], b, c],
    ['learned decision function', 'true inliers', 'true outliers'],
    prop = matplotlib.font_manager.FontProperties(size = 10),
    loc ='lower right')

subplot.set_title('K-Nearest Neighbours')
subplot.set_xlim((-10, 10))
subplot.set_ylim((-10, 10))
plt.show()

Local outlier factor

AlindGupta

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Machine Learning for Anomaly Detection

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