A Python Toolkit for Scalable Outlier Detection (Anomaly Detection

https://github.com/yzhao062/pyod

A Python Toolkit for Scalable Outlier Detection (Anomaly Detection) http://pyod.readthedocs.io

PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as Outlier Detection or Anomaly Detection. Since 2017, PyOD has been successfully used in various academic researches and commercial products [18] [19] [20]. PyOD is featured for:

• Unified APIs, detailed documentation, and interactive examples across various algorithms.
• Advanced models, including Neural Networks/Deep Learning and Outlier Ensembles.
• Optimized performance with JIT and parallelization when possible, using numba and joblib.
• Compatible with both Python 2 & 3 (scikit-learn compatible as well).

Important Notes: PyOD contains neural network based models, e.g., AutoEncoders, which are implemented in Keras. However, PyOD would NOT install Keras and/or TensorFlow automatically. This reduces the risk of damaging your local copies. If you want to use neural net based models, you should install Keras and back-end libraries like TensorFlow manually. An instruction is provided: neural-net FAQ. Similarly, some models, e.g., XGBOD, depend on xgboost, which would NOT be installed by default.
Key Links and Resources:

• View the latest codes on Github
• Execute Interactive Jupyter Notebooks
• Anomaly Detection Resources

Table of Contents:

• Quick Introduction
• Installation
• API Cheatsheet & Reference
• Algorithm Benchmark
• Quick Start for Outlier Detection
• Quick Start for Combining Outlier Scores from Various Base Detectors
• How to Contribute and Collaborate

Citing PyOD:
If you use PyOD in a scientific publication, we would appreciate citations to the following paper:

@article{zhao2019pyod,
  title={PyOD: A Python Toolbox for Scalable Outlier Detection},
  author={Zhao, Yue and Nasrullah, Zain and Li, Zheng},
  journal={arXiv preprint arXiv:1901.01588},
  year={2019},
  url={https://arxiv.org/abs/1901.01588}
}

It is currently under review at JMLR (machine learning open-source software track). See preprint.