umang-garg21 / Fair-MisGAIN

Codebase for "Fair-GAIN" for fair ML predictions.

Fair "Generative Adversarial Imputation Networks (GAIN)"

Authors: Umang Garg

This directory contains implementations of GAIN framework for imputation using advanced datasets like UCI Adult, COMPAS.

To run the pipeline for training and evaluation on GAIN framwork, simply run python -m main.py.

Note that any model architecture can be used as the generator and discriminator model such as multi-layer perceptrons or CNNs.

Command inputs:

• data_name: letter, spam or adult
• miss_rate: probability of missing components
• batch:size: batch size
• hint_rate: hint rate
• alpha: hyperparameter
• iterations: iterations
• deep_analysis: True or False for Model validity
• imputer type: select imputer
• drop_f: option to drop features
• runs: number of runs

Example command

python main.py --data_name adult  --miss_rate 0.1 --batch_size 128 --hint_rate 1  --alpha 10 --iterations 1000 --drop_f 0 1 5 10 13 --deep_analysis True --runs 10

Outputs

• imputed_data_x_lst: imputed data list
• rmse_lst: Root Mean Squared Error list