This repo contains the code to perform fair kernel learning.
The model is based on Fair Kernel Learning and Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness. The code is modified from repo fairgp_code.
To reproduce the results, please read data and parameters that we provided from files, instead of generate new parameters.
Step 1: Run pip install -r requirements.txt to set up environment.
Step 2: Run fair_KRR.py. Attention: some of the code is used to generating parameters for grid search, but if you run the file directly, it will overwrite all the generated parameters by reading parameters from files to reproduce result from the report.
To use approximation of HSIC to speed up the calculation, set auto=True in HSIC, pvalue = Hsic().test(s_train, y_train_pred, workers=-1, auto=True)(from perform_kernel_regression_fair_learning.py, function fair_regression).
data_preprocessing/py: data pre-processing, modified from repo Communities-Crime
visual: contains file to visualize result.