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test_disparate_impact_remover.py
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test_disparate_impact_remover.py
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import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC as SVM
from sklearn.preprocessing import MinMaxScaler
from aif360.algorithms.preprocessing import DisparateImpactRemover
from aif360.datasets import AdultDataset
from aif360.metrics import ClassificationMetric
def test_repair0():
ad = AdultDataset(protected_attribute_names=['sex'],
privileged_classes=[['Male']], categorical_features=[],
features_to_keep=['age', 'education-num'])
di = DisparateImpactRemover(repair_level=0.)
ad_repd = di.fit_transform(ad)
assert ad_repd == ad
def test_adult():
protected = 'sex'
ad = AdultDataset(protected_attribute_names=[protected],
privileged_classes=[['Male']], categorical_features=[],
features_to_keep=['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week'])
scaler = MinMaxScaler(copy=False)
# ad.features = scaler.fit_transform(ad.features)
test, train = ad.split([16281])
assert np.any(test.labels)
train.features = scaler.fit_transform(train.features)
test.features = scaler.transform(test.features)
index = train.feature_names.index(protected)
X_tr = np.delete(train.features, index, axis=1)
X_te = np.delete(test.features, index, axis=1)
y_tr = train.labels.ravel()
di = DisparateImpactRemover(repair_level=1.0)
train_repd = di.fit_transform(train)
# train_repd2 = di.fit_transform(train)
# assert train_repd == train_repd2
test_repd = di.fit_transform(test)
assert np.all(train_repd.protected_attributes == train.protected_attributes)
lmod = LogisticRegression(class_weight='balanced')
# lmod = SVM(class_weight='balanced')
lmod.fit(X_tr, y_tr)
test_pred = test.copy()
test_pred.labels = lmod.predict(X_te)
X_tr_repd = np.delete(train_repd.features, index, axis=1)
X_te_repd = np.delete(test_repd.features, index, axis=1)
y_tr_repd = train_repd.labels.ravel()
assert (y_tr == y_tr_repd).all()
lmod.fit(X_tr_repd, y_tr_repd)
test_repd_pred = test_repd.copy()
test_repd_pred.labels = lmod.predict(X_te_repd)
p = [{protected: 1}]
u = [{protected: 0}]
cm = ClassificationMetric(test, test_pred, privileged_groups=p, unprivileged_groups=u)
before = cm.disparate_impact()
# print('Disparate impact: {:.4}'.format(before))
# print('Acc overall: {:.4}'.format(cm.accuracy()))
repaired_cm = ClassificationMetric(test_repd, test_repd_pred, privileged_groups=p, unprivileged_groups=u)
after = repaired_cm.disparate_impact()
# print('Disparate impact: {:.4}'.format(after))
# print('Acc overall: {:.4}'.format(repaired_cm.accuracy()))
assert after > before
assert abs(1 - after) <= 0.2