fairness-datasets 0.4.0

PyTorch dataset wrapper for the

fairness-datasets

PyTorch dataset wrappers for the several popular datasets from fair machine learning research.

The following datasets are wrapped:

• Adult (Census Income).
• Default
• Law School (data from here)
• SouthGerman

Installation

pip install fairness-datasets

Basic Usage

from fairnessdatasets import Adult

# load (if necessary, download) the Adult training dataset 
train_set = Adult(root="datasets", download=True)
# load the test set
test_set = Adult(root="datasets", train=False, download=True)

inputs, target = train_set[0]  # retrieve the first sample of the training set

# iterate over the training set
for inputs, target in iter(train_set):
    ...  # Do something with a single sample

# use a PyTorch data loader
from torch.utils.data import DataLoader

loader = DataLoader(test_set, batch_size=32, shuffle=True)
for epoch in range(100):
    for inputs, targets in iter(loader):
        ...  # Do something with a batch of samples

You can use Adult(..., raw=True) to turn off the one-hot encoding and z-score normalization applied by the Adult class by default.

The remaining dataset classes can be used in the same way as Adult. However, these datasets don't come with a fixed train/test split, so that the dataset instances always contain all data. To create a train/test split, use

from fairnessdatasets import Default
from torch.utils.data import random_split

dataset = Default(root="datasets", download=True)

rng = torch.Generator().manual_seed(42)  # for reproducible results
train_set, test_set = random_split(dataset, [0.7, 0.3], generator=rng)

Advanced Usage

Turn off status messages while downloading the dataset:

Adult(root=..., output_fn=None)

Use the logging module for logging status messages while downloading the dataset instead of placing the status messages on sys.stdout.

import logging

Adult(root=..., output_fn=logging.info)