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.
anlearn depends on scikit-learn and it's dependencies scipy and numpy.
Requirements:
- python >=3.6
- scikit-learn
- scipy
- numpy
Requirements for every supported python version with version and hashes could be found in requirements folder.
We're using pip-tools for generating requirements files.
pip install anlearn
git clone https://github.com/gaussalgo/anlearn
cd anlearn
Instalil anlearn.
pip install .
or by using poetry
poetry install
You can find documentation at Read the Docs: docs.
Do you have any questions, suggestions, or want to chat? Feel free to contact us via Github, Gitter, or email.
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 its license.