Metadata-Version: 2.4
Name: badr
Version: 0.1.1
Summary: Badr is a framework for training fair machine-learning models that minimize a chosen fairness metric while preserving Pareto-optimal performance across sensitive groups.
Project-URL: Homepage, https://github.com/AdaptiveDecisionMakingGroup/badr
Project-URL: Documentation, https://badr.readthedocs.io/en/latest/
Project-URL: Repository, https://github.com/AdaptiveDecisionMakingGroup/badr
Author: Samuel Vaiter, Yassine Laguel
Author-email: Sofiane Tanji <sofianetanji2@gmail.com>
License-Expression: GPL-3.0-or-later
License-File: LICENSE
Keywords: bilevel-optimization,fairness,machine-learning,pareto-optimization,scikit-learn
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.11
Requires-Dist: cvxpy>=1.6.5
Requires-Dist: folktables>=0.0.12
Requires-Dist: jax>=0.6
Requires-Dist: matplotlib>=3.8
Requires-Dist: numpy<2.1,>=1.26
Requires-Dist: pandas<3,>=2.2
Requires-Dist: scikit-learn>=1.4
Requires-Dist: tqdm>=4.67.1
Requires-Dist: tueplots>=0.2.1
Description-Content-Type: text/markdown

<div align="center">
  <h1>BADR - Bilevel Adaptive Rescalarization</h1>
  <h4>Fairness-Informed Pareto Optimization</h4>
</div>

[![Python](https://img.shields.io/badge/Python-blue?logo=python&logoColor=yellow&style=for-the-badge)](https://www.python.org)
[![Scikit Learn](https://img.shields.io/badge/ScikitLearn-red?logo=scikit-learn&style=for-the-badge)](https://scikit-learn.org)
![License](https://img.shields.io/badge/License-GPLv3-blue.svg?style=for-the-badge)

``badr`` is a Python package that transforms a large range of estimators into **fair** and **Pareto-efficient** estimators.

Have a look at ``badr`` [documentation](https://badr.readthedocs.io/en/latest/)!

## Citations
If you find this repository useful, or you use it in your research, please consider citing the following paper:

```
@article{tanji2026fairness,
  title   = {Fairness-informed Pareto Optimization: An Efficient Bilevel Framework},
  author  = {Tanji, Sofiane and Vaiter, Samuel and Laguel, Yassine},
  journal = {arXiv preprint arXiv:2601.13448},
  year    = {2026}
}
```