anlearn - Anomaly learn

In Gauss Algorithmic, we’re working on many anomaly/fraud detection projects using open-source tools. We decided to put our two cents in and “tidy up” some of our code snippets, add documentation, examples, and release them as an open-source package. So let me introduce anlearn. It aims to offer multiple interesting anomaly detection methods in familiar scikit-learn API so you could quickly try some anomaly detection experiments yourself.

So far, this package is an alpha state and ready for your experiments.

Do you have any questions, suggestions, or want to chat? Feel free to contact us via Github, Gitter, or email.

Content

• Home
• Installation
  • Intallation options

User guide

• LODA: Lightweight on-line detector of anomalies
  • LODA parameters
  • Projections and histograms
  • Explaining the cause of an anomaly
  • Using LODA on large datasets
  • References
  • Examples using anlearn.loda.LODA
• Gallery

Developer guide

• Developers Guide
  • Tools
  • Setting-up the developers’ environment
  • Tests
  • Documentation
• API reference
  • anlearn.loda
  • anlearn.stats

Indices and tables

• Index

• Module Index

• Search Page

License

GNU Lesser General Public License v3 or later (LGPLv3+)

anlearn Copyright (C) 2020 Gauss Algorithmic a.s.

This package is in alpha state and comes with ABSOLUTELY NO WARRANTY. This is free software, and you are welcome to use, redistribute it and contribute under certain conditions of it’s license.