def test_epsilon_all_groups():
def custom_preprocessing(df):
# slight workaround for non-binary protected attribute
# feature should be categorical but protected attribute should be numerical
mapping = {
'Black': 0,
'White': 1,
'Asian-Pac-Islander': 2,
'Amer-Indian-Eskimo': 3,
'Other': 4
}
df['race-num'] = df.race.map(mapping)
return df.fillna('Unknown')
nonbinary_ad = AdultDataset(
protected_attribute_names=['sex', 'native-country', 'race-num'],
privileged_classes=[['Male'], ['United-States'], [1]],
categorical_features=[
'workclass', 'education', 'marital-status', 'occupation',
'relationship', 'race'
],
custom_preprocessing=custom_preprocessing)
# drop redundant race feature (not relevant to this test)
index = nonbinary_ad.feature_names.index('race-num')
nonbinary_ad.features = np.delete(nonbinary_ad.features, index, axis=1)
nonbinary_ad.feature_names = np.delete(nonbinary_ad.feature_names, index)
_, nonbinary_test = nonbinary_ad.split([32561], shuffle=False)
dataset_metric = BinaryLabelDatasetMetric(nonbinary_test)
eps_data = dataset_metric.smoothed_empirical_differential_fairness()
assert eps_data == 2.063813731996515 # verified with reference implementation
def get_bias_amplification(train_data, prediction_data):
privileged_groups= [{'sex':1}]
unprivileged_groups= [{'sex': 0}]
train_metrics = BinaryLabelDatasetMetric(train_data,
unprivileged_groups=unprivileged_groups,
privileged_groups=privileged_groups)
prediction_metrics = BinaryLabelDatasetMetric(prediction_data,
unprivileged_groups=unprivileged_groups,
privileged_groups=privileged_groups)
tedf = train_metrics.smoothed_empirical_differential_fairness()
pedf = prediction_metrics.smoothed_empirical_differential_fairness()
bias_amp = pedf - tedf
return bias_amp
