Getting started

This section demonstrates the usage of unfair-data-generator.

Installation

To install unfair-data-generator with pip, run the following command:

pip install unfair-data-generator

Usage

The following example demonstrates how to generate a biased dataset and evaluate fairness using unfair-data-generator.

from unfair_data_generator.unfair_classification import make_unfair_classification
from unfair_data_generator.util.helpers import get_params_for_certain_equality_type
from unfair_data_generator.util.model_trainer import train_and_evaluate_model_with_classifier
from unfair_data_generator.util.visualizer import (
    visualize_TPR_FPR_metrics,
    visualize_accuracy,
    visualize_groups_separately,
    visualize_group_classes
)

# Configure dataset parameters
fairness_type = "Demographic parity"
n_sensitive_groups = 3

# Generate group-specific parameters for fairness violation
group_params = get_params_for_certain_equality_type(fairness_type, n_sensitive_groups)

# Generate biased dataset
X, y, Z, centroids = make_unfair_classification(
    n_samples=5000,
    n_features=10,
    n_informative=3,
    n_leaky=2,
    random_state=42,
    group_params=group_params,
    return_sensitive_group_centroids=True,
)

# Visualize group-specific patterns
visualize_groups_separately(X, y, Z)
visualize_group_classes(X, y, Z, centroids)

# Train model and evaluate fairness
metrics = train_and_evaluate_model_with_classifier(X, y, Z)

# Visualize fairness metrics
title = f"{fairness_type} with {n_sensitive_groups} sensitive groups"
visualize_TPR_FPR_metrics(metrics, title)
visualize_accuracy(metrics, title)