Source code for alibi_detect.od.isolationforest

import logging
import numpy as np
from sklearn.ensemble import IsolationForest
from typing import Dict, Union
from alibi_detect.base import BaseDetector, FitMixin, ThresholdMixin, outlier_prediction_dict

logger = logging.getLogger(__name__)

[docs] class IForest(BaseDetector, FitMixin, ThresholdMixin):
[docs] def __init__(self, threshold: float = None, n_estimators: int = 100, max_samples: Union[str, int, float] = 'auto', max_features: Union[int, float] = 1., bootstrap: bool = False, n_jobs: int = 1, data_type: str = 'tabular' ) -> None: """ Outlier detector for tabular data using isolation forests. Parameters ---------- threshold Threshold used for outlier score to determine outliers. n_estimators Number of base estimators in the ensemble. max_samples Number of samples to draw from the training data to train each base estimator. If int, draw 'max_samples' samples. If float, draw 'max_samples * number of features' samples. If 'auto', max_samples = min(256, number of samples) max_features Number of features to draw from the training data to train each base estimator. If int, draw 'max_features' features. If float, draw 'max_features * number of features' features. bootstrap Whether to fit individual trees on random subsets of the training data, sampled with replacement. n_jobs Number of jobs to run in parallel for 'fit' and 'predict'. data_type Optionally specify the data type (tabular, image or time-series). Added to metadata. """ super().__init__() if threshold is None: logger.warning('No threshold level set. Need to infer threshold using `infer_threshold`.') self.threshold = threshold self.isolationforest = IsolationForest(n_estimators=n_estimators, max_samples=max_samples, max_features=max_features, bootstrap=bootstrap, n_jobs=n_jobs) # set metadata self.meta['detector_type'] = 'outlier' self.meta['data_type'] = data_type self.meta['online'] = False
[docs] def fit(self, X: np.ndarray, sample_weight: np.ndarray = None ) -> None: """ Fit isolation forest. Parameters ---------- X Training batch. sample_weight Sample weights. """, sample_weight=sample_weight)
[docs] def infer_threshold(self, X: np.ndarray, threshold_perc: float = 95. ) -> None: """ Update threshold by a value inferred from the percentage of instances considered to be outliers in a sample of the dataset. Parameters ---------- X Batch of instances. threshold_perc Percentage of X considered to be normal based on the outlier score. """ # compute outlier scores iscore = self.score(X) # update threshold self.threshold = np.percentile(iscore, threshold_perc)
[docs] def score(self, X: np.ndarray) -> np.ndarray: """ Compute outlier scores. Parameters ---------- X Batch of instances to analyze. Returns ------- Array with outlier scores for each instance in the batch. """ return - self.isolationforest.decision_function(X)
[docs] def predict(self, X: np.ndarray, return_instance_score: bool = True) \ -> Dict[Dict[str, str], Dict[np.ndarray, np.ndarray]]: """ Compute outlier scores and transform into outlier predictions. Parameters ---------- X Batch of instances. return_instance_score Whether to return instance level outlier scores. Returns ------- Dictionary containing ``'meta'`` and ``'data'`` dictionaries. - ``'meta'`` has the model's metadata. - ``'data'`` contains the outlier predictions and instance level outlier scores. """ # compute outlier scores iscore = self.score(X) # values above threshold are outliers outlier_pred = (iscore > self.threshold).astype(int) # populate output dict od = outlier_prediction_dict() od['meta'] = self.meta od['data']['is_outlier'] = outlier_pred if return_instance_score: od['data']['instance_score'] = iscore return od