pip install fairlearn
# OR
conda install -c conda-forge fairlearn

from fairlearn.metrics import MetricFrame
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier

classifier = DecisionTreeClassifier(min_samples_leaf=10, max_depth=4)
classifier.fit(X, y_true)
y_pred = classifier.predict(X)

mf = MetricFrame(
    metrics=accuracy_score,
    y_true=y_true,
    y_pred=y_pred,
    sensitive_features=sex,
)
print(mf.by_group)
print(f"Disparity (max-min): {mf.difference()}")

from fairlearn.metrics import selection_rate

sr = MetricFrame(
    metrics=selection_rate,
    y_true=y_true,
    y_pred=y_pred,
    sensitive_features=sex,
)
print(sr.by_group)
# Demographic Parity Difference (DPD)
print(f"DPD: {sr.difference()}")

from fairlearn.metrics import (
    true_positive_rate,
    false_positive_rate,
    MetricFrame,
)

mf = MetricFrame(
    metrics={
        "TPR": true_positive_rate,
        "FPR": false_positive_rate,
        "selection_rate": selection_rate,
    },
    y_true=y_true,
    y_pred=y_pred,
    sensitive_features=sex,
)
print(mf.by_group)

from fairlearn.reductions import DemographicParity, ExponentiatedGradient

constraint = DemographicParity()
mitigator = ExponentiatedGradient(classifier, constraint)
mitigator.fit(X, y_true, sensitive_features=sex)
y_pred_mitigated = mitigator.predict(X)

from fairlearn.postprocessing import ThresholdOptimizer

postprocess = ThresholdOptimizer(
    estimator=classifier,
    constraints="demographic_parity",
    prefit=True,
)
postprocess.fit(X, y_true, sensitive_features=sex)
y_pred_pp = postprocess.predict(X, sensitive_features=sex)

def assert_fairness(y_true, y_pred, sensitive, max_dpd=0.10):
    sr = MetricFrame(
        metrics=selection_rate,
        y_true=y_true,
        y_pred=y_pred,
        sensitive_features=sensitive,
    )
    dpd = sr.difference()
    if dpd > max_dpd:
        raise AssertionError(
            f"Demographic Parity Difference {dpd:.3f} exceeds budget {max_dpd}"
        )

assert_fairness(y_true, y_pred, sex, max_dpd=0.10)