Metadata-Version: 2.1
Name: hypper
Version: 0.0.5
Summary: Hypergraph-based data mining tool for binary classification.
Home-page: https://github.com/hypper-team/hypper
Author: Szymon Janowski, Paweł Misiorek
Author-email: szy.janowski@gmail.com, pawel.misiorek@put.poznan.pl
Project-URL: Bug Tracker, https://github.com/hypper-team/hypper/issues
Project-URL: Documentation, https://hypper-team.github.io/hypper.html
Keywords: hypergraphs machine-learning undersampling feature-selection classification
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Provides-Extra: documentation
Provides-Extra: develop
Provides-Extra: benchmarking
Provides-Extra: all
License-File: LICENSE

![](logo/logo1.png)

![PyPI](https://img.shields.io/pypi/v/hypper) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/hypper) ![PyPI - Wheel](https://img.shields.io/pypi/wheel/hypper) ![build](https://github.com/hypper-team/hypper/actions/workflows/main.yml/badge.svg) [![PyPI Downloads](https://static.pepy.tech/personalized-badge/hypper?period=total&units=none&left_color=grey&right_color=yellowgreen&left_text=downloads)](https://pepy.tech/project/hypper) ![PyPI - License](https://img.shields.io/pypi/l/hypper)

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
```bash
pip install hypper
```
Local installations
``` bash
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:
[![](https://colab.research.google.com/assets/colab-badge.svg)  1. Introduction to data mining with Hypper](https://colab.research.google.com/drive/1JntX8z3-e0qhCSjxpjYnPmfR2Iy09e15?usp=sharing)

## Testing
```bash
pytest
```
## Important links
* Source code - [https://github.com/hypper-team/hypper](https://github.com/hypper-team/hypper)
* Documentation - [https://hypper-team.github.io/hypper.html](https://hypper-team.github.io/hypper.html)

## Citation
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```
