Tutorial

The examples folder contains tutorials from different fields illustrating how to use the package.

Minimal example

First, you need to import the relevant packages

from inFairness.distances import SVDSensitiveSubspaceDistance, EuclideanDistance
from inFairness.fairalgo import SenSeI

The inFairness.distances module implements various distance metrics on the input and the output spaces, and the inFairness.fairalgo implements various individually fair learning algorithms with SenSeI being one particular algorithm.

Thereafter, we instantiate and fit the distance metrics on the training data, and

distance_x = distances.SVDSensitiveSubspaceDistance()
distance_y = distances.EuclideanDistance()

distance_x.fit(X_train=data, n_components=50)

# Finally instantiate the fair algorithm
fairalgo = SenSeI(network, distance_x, distance_y, lossfn, rho=1.0, eps=1e-3, lr=0.01, auditor_nsteps=100, auditor_lr=0.1)

Finally, you can train the fairalgo as you would train your standard PyTorch deep neural network:

fairalgo.train()

for epoch in range(EPOCHS):
    for x, y in train_dl:
        optimizer.zero_grad()
        result = fairalgo(x, y)
        result.loss.backward()
        optimizer.step()