PyOD: A Python Toolbox for Scalable Outlier Detection

TL;DR

PyOD is a comprehensive Python toolbox that offers a wide array of scalable outlier detection algorithms, including ensemble and neural network methods, with a focus on usability, robustness, and scalability for practitioners and researchers.

Contribution

It provides a unified, well-documented API for diverse outlier detection algorithms, emphasizing best practices like testing, parallelization, and maintainability.

Findings

Includes a broad range of outlier detection algorithms

Supports scalable and parallel processing

Designed for ease of use and integration

Abstract

PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for use by both practitioners and researchers. With robustness and scalability in mind, best practices such as unit testing, continuous integration, code coverage, maintainability checks, interactive examples and parallelization are emphasized as core components in the toolbox's development. PyOD is compatible with both Python 2 and 3 and can be installed through Python Package Index (PyPI) or https://github.com/yzhao062/pyod.