fair-loss 0.2

A fair loss function

A fair PyTorch loss function

The goal of this loss function is to take fairness into account during the training of a PyTorch model. It works by adding a fairness measure to a regular loss value, following this equation:

Installation

pip install fair-loss

Example

import torch
import torch.nn.functional as F
import numpy as np
from fair_loss import FairLoss


def accuracy(input, targs):
    return torch.true_divide((input == targs).sum(), input.shape[0])


model = torch.nn.Sequential(torch.nn.Linear(5, 1), torch.nn.ReLU())
data = np.random.randint(5, size=(100, 5)).astype("float")
data = torch.tensor(data, requires_grad=True, dtype=torch.float)
y_true = np.random.randint(5, size=(100, 1)).astype("float")
y_true = torch.tensor(y_true, requires_grad=True)
y_pred = model(data)
# Let's say the sensitive attribute is in the second dimension
dim = 1
loss = F.mse_loss(y_pred, y_true)
loss = FairLoss(data[:, dim], loss, y_pred, y_true, accuracy)
loss.backward()

Documentation

See the documentation.