Metadata-Version: 2.4
Name: maddlib
Version: 1.1.0
Summary: A Python package to compute MADD metric-related functions
Home-page: https://github.com/melinaverger/maddlib
Author: Mélina Verger
Author-email: melina.verger@insa-lyon.fr
License: CC BY-NC 4.0
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10.4
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: scikit-learn
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: seaborn
Requires-Dist: matplotlib
Provides-Extra: dev
Requires-Dist: twine>=4.0.2; extra == "dev"
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Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
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Dynamic: license
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# maddlib

[![Python version](https://img.shields.io/badge/python->3.10.4-brightgreen.svg)](https://www.python.org/)

This repository contains the source code of the Python package related to the [MADD metric](https://github.com/melinaverger/MADD).

## Installation

This package is available on [pypi](https://pypi.org/project/maddlib/) and [GitHub](https://github.com/melinaverger/maddlib).

It can be installed using `pip` (with command line `pip install maddlib` or `python3 -m pip install maddlib`).

If an error occurs, please check first that your Python version is greater than 3.10.4.

## Getting started

Some example notebooks are in `tutorials` folder. You can use the MADD to evaluate algorithmic fairness or to mitigate algorithmic unfairness.

You can find the full documentation here: [https://melinaverger.github.io/documentation-maddlib/](https://melinaverger.github.io/documentation-maddlib/).

## Reference

If you are using this package, please cite:

> M. Verger, S. Lallé, F. Bouchet, and V. Luengo. Is Your Model ”MADD”? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models. In M. Feng, T. Käser, and P. Talukdar, editors, *Proceedings of the 16th International Conference on Educational Data Mining*, pages 91–102, Bengaluru, India, July 2023. International Educational Data Mining Society.

```LaTex
@inproceedings{Verger2023,  
    title = {Is Your Model ``MADD''? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models},  
    author = {M\'{e}lina Verger and S\'{e}bastien Lall\'{e} and Fran\c{c}ois Bouchet and Vanda Luengo},  
    booktitle = {Proceedings of the 16th International Conference on Educational Data Mining},  
    editor = {Mingyu Feng and Tanja KÃ¤ser and Partha Talukdar},  
    doi = {10.5281/zenodo.8115786},  
    isbn = {978-1-7336736-4-8},  
    month = {July},  
    address = {Bengaluru, India},  
    pages = {91--102},  
    publisher = {International Educational Data Mining Society},  
    year = {2023}  
}
```

## License

<a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.
