Implementing Isolation Forests For Anomaly Detection In Python

In this short blog, we'll tackle an obscure but fascinating subject in the field of data science - Isolation Forests for Anomaly Detection. In machine learning, anomaly detection (also known as outlier detection) is the identification of rare items or events that raise suspicions by tabled to differ significantly from the majority of the data. The Isolation Forest algorithm 'isolates' observations by arbitrarily selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

Let's dive in.

Understanding Isolation Forests

To implement an Isolation Forest, we'll use Python and the Scikit-learn library. This algorithm works by creating binary decision trees, that is, by isolating observations. It does this randomly, and anomalies are usually isolated more promptly, leading to shorter paths in the tree. The path length, averaged over multiple trees, is a measure of normality and aids in our decision making.

Installation

First, ensure you have the necessary libraries installed. If not, run the following command in your terminal:

pip install numpy pandas matplotlib scikit-learn

Data Importation and Preparation

Next up, we'll import some credit card data using Pandas and prepare it for our model:

import pandas as pd

data = pd.read_csv('creditcard.csv')

# Let's assume anything above 2 standard deviations is an anomaly

anomaly_data = data[data['Amount'] > data['Amount'].mean() + 2 * data['Amount'].std()]
normal_data = data[data['Amount'] <= data['Amount'].mean() + 2 * data['Amount'].std()]

Model Implementation

We now implement the Isolation Forest using Scikit-learn. We fit on the normal data and predict on the anomaly data:

from sklearn.ensemble import IsolationForest

iso_forest = IsolationForest(contamination=0.1)
iso_forest.fit(normal_data.drop(['Class'], axis=1))

pred = iso_forest.predict(anomaly_data.drop(['Class'], axis=1))

Congrats! We successfully implemented an Isolation Forest for anomaly detection using Python! This brief snippet is a solid starting point for anyone venturing into the world of outlier detection in data science. Experiment, explore, and expand your knowledge from here. Happy learning!