Hypper is a data-mining Python library for binary classification. It uses hypergraph-based methods to explore datasets for the purpose of undersampling, feature selection and binary classification.

Hypper provides an easy-to-use interface familiar to well-recognized Scikit-Learn API.

The primary goal of this library is to provide a tool for handling datasets consisting of mainly categorical features. Novel hypergraph-based methods proposed in the Hypper library were benchmarked against the alternative solutions and achieved satisfactory results. More details can be found in scientific papers presented in the section below.

Installation

pip install hypper

Local installations

pip install -e .['documentation'] # documentation
pip install -e .['develop'] # development (with testing)
pip install -e .['benchmarking'] # benchmarking scripts
pip install -e .['all'] # install everything

Tutorials:

1. Introduction to data mining with Hypper

Testing

pytest

Important links

• Source code - https://github.com/hypper-team/hypper
• Documentation - https://hypper-team.github.io/hypper.html

Citation