ConstraintGBM

Introduction

ConstraintGBM is a machine learning project that focus on the fairness and Neyman-Pearson tasks. The project is based on two popular gradient boosting libraries, XGBoost and LightGBM. Our project customizes the objective function of XGBoost and LightGBM to include additional information like the last predicted score. The customization enables us to address fairness and Neyman-Pearson tasks in our predictions.

Features

• Customized objective function in C++.
• The main algorithm logic is implemented in Python, hence only need to call xgboost and lightgbm's training api.
  • Only support Python interface

Installation

Requirements

All of our installed dependencies are consistent with xgboost-2.0.0-dev and lightgbm-3.3.5

Easy installation

To install ConstrainGBM, simply clone this repository and run the following command:

bash install.sh

Usage

Some examples are put under LightGBM-3.3.5/np_fair_test and xgboost/np_fair_test folder.

All experimental results within the paper can be reproduced with the following commands:

python ./test_adult_fair/adult_fair_lgb_random_para.py
python ./test_adult_fair/adult_fair_xgb_random_para.py

python ./test_compas_fair/compas_fair_lgb_random_para.py
python ./test_compas_fair/compas_fair_xgb_random_para.py

python ./test_credit_np/credit_np_lgb_random_para.py
python ./test_credit_np/credit_np_xgb_random_para.py

python ./test_drybean_np/drybean_np_lgb_random_para.py
python ./test_drybean_np/drybean_np_xgb_random_para.py

Future work

citing this package

Disclaimer

• ConstraintGBM is a research software, therefore it should not be used in production.
• Please open an issue if you find any problems, developers will try to fix and find alternatives.

Acknowledgements

Many thanks to the very active XGBoost and LightGBM community for their enthusiastic answers to our questions. Many thanks to chatgpt for assisting us in writing the code.