PyOD

Overview

PyOD is a Python library designed for detecting anomalies or outliers in multivariate data. Established in 2017, it offers over 50 detection algorithms ranging from classical methods like LOF to cutting-edge techniques like ECOD and DIF. The library is engineered for high performance, utilizing `numba` and `joblib` for JIT compilation and parallel processing, enabling fast training and prediction through the SUOD framework. Version 2 incorporates a PyTorch-based framework for deep learning models, expanding its capabilities. PyOD also leverages LLM-based model selection to automate tuning. It integrates with ADBench for comprehensive benchmarking and is compatible with distributed systems like Databricks. The library supports various probabilistic, linear, and proximity-based models, providing a unified interface for outlier detection tasks.

Common tasks

Outlier Detection Anomaly Detection Data Preprocessing

FAQ

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What is PyOD?

PyOD is a Python library for detecting anomalies/outliers in multivariate data, offering a wide range of detection algorithms.

How do I install PyOD?

You can install PyOD using pip: `pip install pyod`.

What types of algorithms does PyOD support?

PyOD includes more than 50 detection algorithms, from classical LOF to cutting-edge ECOD and DIF, covering probabilistic, linear, and proximity-based methods.

Can PyOD be used with large datasets?

Yes, PyOD is designed for scalability and high performance, leveraging numba and joblib for JIT compilation and parallel processing, suitable for large datasets.

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