models/unsupervised/anomaly/isolation_forest.py

"""
isolation_forest.py

This module defines an Isolation Forest model for anomaly detection. 
Isolation Forest is an efficient and effective algorithm for identifying 
outliers in high-dimensional datasets.

Key Features:
- Utilizes a tree-based approach to isolate anomalies.
- Efficient for both large datasets and high-dimensional spaces.
- Automatically determines the expected proportion of anomalies.

Parameters:
    - n_estimators (int): Number of base estimators in the ensemble.
        - Default: 100.
    - contamination (str or float): Expected proportion of outliers in the data.
        - Default: 'auto' (automatically inferred based on dataset size).
    - max_samples (int or float): Number of samples to draw for training each estimator.
        - Default: 'auto' (uses min(256, number of samples)).

Default Configuration:
    - n_estimators=100: Adequate for most datasets.
    - contamination='auto': Automatically estimates the proportion of outliers.
"""

from sklearn.ensemble import IsolationForest

# Define the Isolation Forest estimator
estimator = IsolationForest(
    n_estimators=100,       # Default number of trees
    contamination='auto',   # Automatically estimates the contamination proportion
    random_state=42         # Ensures reproducibility
)