balif 0.3.0

Bayesian Active Learning Isolation Forest

BALIF: Bayesian Active Learning Isolation Forest

Version: 0.1.2
License: MIT

Description

Convert unsupervised tree ensembles into Bayesian Anomaly Detectors (BAD) that can be updated dynamically. BAD models are build on top of the popular PyOD and keep the original interface, while adding cool new capabilities for:

• Weakly Supervised Learning
• Active Learning
• Lifelong Learning

Installation

Install BALIF using pip:

pip install balif

Usage

PyOD Compatibility

BAD model maintain the same interface as PyOD, making it easy to integrate into existing workflows. The core methods like fit(), decision_function(), and predict() work exactly the same way as in standard PyOD models. This allows users to seamlessly switch between regular PyOD models and BALIF's Bayesian versions with minimal code changes.

from pyod.models.iforest import IForest
from balif import BADIForest
import numpy as np

# Generate some data
X_inliers = np.random.randn(1000, 5)
X_outliers = np.random.uniform(low=-4, high=4, size=(50, 5))
X_train = np.concatenate([X_inliers, X_outliers], axis=0)

# BAD model follow the PyOD interface
pyod_model = IForest().fit(X_train)
bad_model = BADIForest().fit(X_train)

# Get anomaly scores
scores = pyod_model.decision_function(X_train)
scores = bad_model.decision_function(X_train)

# Predict if points are anomalies
predictions = pyod_model.predict(X_train)
predictions = bad_model.predict(X_train)

Incremental Learning with .update()

BAD models support incremental learning through the .update() method, allowing you to update the model with new data without retraining from scratch:

# New labelled data becomes available
X_new = np.random.randn(100, 5)
y_new = np.array([0] * 90 + [1] * 10)  # 0: normal, >=1: anomaly

# Update the model with the new data
bad_model.update(X_new, y_new)

# The model now incorporates knowledge from both datasets
updated_scores = bad_model.decision_function(X_test)

Note: For some applications, it might be necessary to recompute the contamination threshold after updating the model, especially if the distribution of your data changes significantly over time.

Active Learning with the AL Module

BALIF includes an active learning module that helps identify the most informative instances for labeling:

from balif import active_learning, BADIForest

# Generate data and fit model
X_inliers = np.random.randn(1000, 5)
X_outliers = np.random.uniform(low=-4, high=4, size=(50, 5))
X_train = np.concatenate([X_inliers, X_outliers], axis=0)
model = BADIForest().fit(X_train)

# get top-k most interesting points 
queries_idx = active_learning.get_queries_independent(
    model, X_train, interest_method="margin", batch_size=10
)

The active learning module offers several query strategies:

• 'margin': Prioritize instances with predictions close to the decision boundary.
• 'anom': Prioritize instances with high anomaly score
• 'bald': Prioritize instances with high mutual entropy between prediction and parameters

Active learning can significantly reduce the labeling effort while maintaining high model performance.

Batteries included with ODDS dataset

BALIF provides easy access to benchmark anomaly detection datasets from the Outlier Detection DataSets (ODDS) repository:

from balif import odds_datasets

# Show included Datasets from ODDS
for name in odds_datasets.dataset_names:
    X, y = odds_datasets.load(name)
    print(f"DATASET: {dataset}")
    print(f"X: {X.shape}")
    print(f"contamination: {100*y.mean():.2f}%")
    print()