Isolation_Forest.py

from sklearn.ensemble import IsolationForest


class Isolation_Forest:
    """
    Isolation Forest or iForest builds an ensemble of iTrees for a given data set, then anomalies are those instances which have short average path lengths on the iTrees.

    Parameters
    ----------
    params : list
        A list containing three parameters: random_state, n_jobs, and contamination.

    Attributes
    ----------
    random_state : int
        The random seed used for reproducibility.
    n_jobs : int
        The number of CPU cores to use for parallelism.
    contamination : float
        The expected proportion of anomalies in the dataset.

    Examples
    --------
    >>> from Isolation_Forest import Isolation_Forest
    >>> PARAMS = [random_state, n_jobs, contamination]
    >>> model = Isolation_Forest(PARAMS)
    >>> model.fit(X_train)
    >>> predictions = model.predict(test_data)
    """

    def __init__(self, params):
        self.params = params
        self.random_state = self.params[0]
        self.n_jobs = self.params[1]
        self.contamination = self.params[2]

    def _Random(self, seed_value):
        import os

        os.environ["PYTHONHASHSEED"] = str(seed_value)

        import random

        random.seed(seed_value)

        import numpy as np

        np.random.seed(seed_value)

        import tensorflow as tf

        tf.random.set_seed(seed_value)

    def _build_model(self):
        self._Random(0)

        model = IsolationForest(
            random_state=self.random_state,
            n_jobs=self.n_jobs,
            contamination=self.contamination,
        )
        return model

    def fit(self, X):
        """
        Train the Isolation Forest model on the provided data.

        Parameters
        ----------
        X : numpy.ndarray
            Input data for training the model.
        """

        self.model = self._build_model()

        self.model.fit(X)

    def predict(self, data):
        """
        Generate predictions using the trained Isolation Forest model.

        Parameters
        ----------
        data : numpy.ndarray
            Input data for generating predictions.

        Returns
        -------
        numpy.ndarray
            Predicted output data.
        """

        return self.model.predict(data)