fairness-shift's Introduction
This directory is for simulating the proposed pre-processing approach on the synthetic dataset. The program needs PyTorch, CVXPY, Jupyter Notebook, and CUDA. The directory contains a total of 5 files and 1 child directory: 1 README, 3 python files, 1 jupyter notebook, and the child directory containing 6 numpy files for synthetic data. The synthetic data contains training set and test set. ---------------------------------------------------------------------- To simulate the algorithm, please use the jupyter notebook in the directory. ---------------------------------------------------------------------- The jupyter notebook will load the data and train the models. We consider two scenarios: supporting (1) a single metric (DP) and (2) multiple metrics (DP & EO). Each training shows either in-processing-only baseline or our framework (i.e., pre- + in-processing). Note that we use FairBatch [Roh et al., ICLR 2021] as the in-processing baseline that adaptively adjusts batch ratios for fairness. When using our pre-processing, we utilize a SDP solver to find the new data ratio. The solver is defined in our program. Experiments are repeated 5 times each. After the training, the test accuracy and fairness will be shown. The two python files are models.py, utils.py, and FairBatchSampler_Multiple.py. The models.py contains a logistic regression architecture. The utils.py contains a total of three functions for finding example weight, sampling data, and testing the model performances. The FairBatchSampler_Multiple.py contains two classes: CustomDataset and FairBatch. CustomDataset class defines the dataset, and FairBatch class implements the state-of-the-art in-processing technique [Roh et al., ICLR 2021] that adjusts batch ratios for fairness. The detailed explanations about each component have been written in the codes as comments. Thanks!
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