This repo contains code to reproduce the experiments in the paper
La Cava, W. and Moore, J.H. (2020). "Genetic programming approaches to learning fair classifiers." GECCO 2020. doi:10.1145/3377930.3390157, arxiv:2004.13282
The experiments can be run by navigating to analysis/ and running
python submit_jobs.py
check out submit_jobs.py to get help on configuration.
The summary figures are generated in hypervolume_comparison.ipynb, and
the stats tests are in stats.ipynb.
Pareto front comparisons are generated by pareto_front_plots.ipynb.
Aside from the usual (sklearn etc.), we make heavy use of the GerryFair repository and its associated work (https://arxiv.org/abs/1711.05144, https://arxiv.org/abs/1808.08166)
Hypervolume calculations come from DEAP
The GP models are based on feat
The authors would like thank colleagues in the Warren Center for Data Science and the Institute for Biomedical Informatics at Penn for their discussions. This work is supported by NIH grants K99 LM012926-02, R01 LM010098 and R01 AI116794.