yzhao062/pyod
PyOD is a comprehensive Python library for multi-modal anomaly detection, offering 60+ detectors and an agentic workflow for AI agents to drive investigations across various data types.
Awesome AI for Infra › Anomaly Detection
kLabUM/rrcf
rrcf is an open-source Python library offering an implementation of the Robust Random Cut Forest (RRCF) algorithm, a powerful ensemble method for detecting outliers in streaming data. Developed by Guha et al. (2016), RRCF is specifically engineered to handle the complexities of real-time data streams and high-dimensional datasets. Its key features include efficient handling of streaming data, robust performance on high-dimensional inputs with reduced influence from irrelevant dimensions, and graceful management of duplicates or near-duplicates that might otherwise obscure anomalies. The algorithm provides an anomaly scoring mechanism with a clear statistical interpretation, known as collusive displacement (CoDisf). This library facilitates experimentation with the RRCF algorithm, providing core data structures and functionalities for inserting, deleting points, and computing anomaly scores both in batch and streaming contexts. It also allows for the estimation of feature importance by analyzing cut dimensions during CoDisp calculation. Required dependencies include numpy, with optional dependencies like pandas, scipy, scikit-learn, and matplotlib for examples and enhanced functionality.
https://github.com/kLabUM/rrcf
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