def test_adult_fdr():
biased_model = MetaFairClassifier(tau=0,
sensitive_attr=protected,
type='fdr',
seed=123).fit(train)
dataset_bias_test = biased_model.predict(test)
biased_cm = ClassificationMetric(test,
dataset_bias_test,
unprivileged_groups=[{
protected: 0
}],
privileged_groups=[{
protected: 1
}])
fdr1 = biased_cm.false_discovery_rate_ratio()
fdr1 = min(fdr1, 1 / fdr1)
debiased_model = MetaFairClassifier(tau=0.9,
sensitive_attr=protected,
type='fdr',
seed=123).fit(train)
dataset_debiasing_test = debiased_model.predict(test)
debiased_cm = ClassificationMetric(test,
dataset_debiasing_test,
unprivileged_groups=[{
protected: 0
}],
privileged_groups=[{
protected: 1
}])
fdr2 = debiased_cm.false_discovery_rate_ratio()
fdr2 = min(fdr2, 1 / fdr2)
assert (fdr2 >= fdr1)
def test_adult():
np.random.seed(1)
# np.random.seed(9876)
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)
train, test = ad.split([32561])
biased_model = MetaFairClassifier(tau=0, sensitive_attr=protected)
biased_model.fit(train)
dataset_bias_test = biased_model.predict(test)
biased_cm = ClassificationMetric(test,
dataset_bias_test,
unprivileged_groups=[{
protected: 0
}],
privileged_groups=[{
protected: 1
}])
unconstrainedFDR2 = biased_cm.false_discovery_rate_ratio()
unconstrainedFDR2 = min(unconstrainedFDR2, 1 / unconstrainedFDR2)
predictions = [
1 if y == train.favorable_label else -1
for y in dataset_bias_test.labels.ravel()
]
y_test = np.array(
[1 if y == train.favorable_label else -1 for y in test.labels.ravel()])
x_control_test = pd.DataFrame(data=test.features,
columns=test.feature_names)[protected]
acc, sr, unconstrainedFDR = getStats(y_test, predictions, x_control_test)
assert np.isclose(unconstrainedFDR, unconstrainedFDR2)
tau = 0.9
debiased_model = MetaFairClassifier(tau=tau, sensitive_attr=protected)
debiased_model.fit(train)
#dataset_debiasing_train = debiased_model.predict(dataset_orig_train)
dataset_debiasing_test = debiased_model.predict(test)
predictions = list(dataset_debiasing_test.labels)
predictions = [
1 if y == train.favorable_label else -1
for y in dataset_debiasing_test.labels.ravel()
]
y_test = np.array(
[1 if y == train.favorable_label else -1 for y in test.labels.ravel()])
x_control_test = pd.DataFrame(data=test.features,
columns=test.feature_names)[protected]
acc, sr, fdr = getStats(y_test, predictions, x_control_test)
debiased_cm = ClassificationMetric(test,
dataset_debiasing_test,
unprivileged_groups=[{
protected: 0
}],
privileged_groups=[{
protected: 1
}])
fdr2 = debiased_cm.false_discovery_rate_ratio()
fdr2 = min(fdr2, 1 / fdr2)
assert np.isclose(fdr, fdr2)
#print(fdr, unconstrainedFDR)
assert (fdr2 >= unconstrainedFDR2)
