Skip to main content

A fair loss function

Project description

A fair PyTorch loss function

REUSE status PyPI version

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
from fair_loss import FairLoss

model = torch.nn.Sequential(torch.nn.Linear(5, 1), torch.nn.ReLU())
data = torch.randint(0, 5, (100, 5), dtype=torch.float, requires_grad=True)
y_true = torch.randint(0, 5, (100, 1), dtype=torch.float)
y_pred = model(data)
# Let's say the sensitive attribute is in the second dimension
dim = 1
criterion = FairLoss(torch.nn.MSELoss(), data[:, dim].detach().unique(), accuracy)
loss = criterion(data[:, dim], y_pred, y_true)
loss.backward()

Documentation

See the documentation.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for fair-loss, version 0.5
Filename, size File type Python version Upload date Hashes
Filename, size fair_loss-0.5-py3-none-any.whl (16.3 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size fair_loss-0.5.tar.gz (29.8 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page