def test_adv_debias_old():
"""Test that the predictions of the old and new AdversarialDebiasing match.
"""
tf.reset_default_graph()
sess = tf.Session()
old_adv_deb = OldAdversarialDebiasing(unprivileged_groups=[{
'sex': 0
}],
privileged_groups=[{
'sex': 1
}],
scope_name='old_classifier',
sess=sess,
num_epochs=5,
seed=123)
old_preds = old_adv_deb.fit_predict(adult)
sess.close()
adv_deb = AdversarialDebiasing('sex', num_epochs=5, random_state=123)
new_preds = adv_deb.fit(X, y).predict(X)
adv_deb.sess_.close()
assert np.allclose(old_preds.labels.flatten(), new_preds)
def test_adv_debias_grid():
"""Test that the new AdversarialDebiasing works in a grid search (and that
debiasing results in reduced accuracy).
"""
adv_deb = AdversarialDebiasing('sex', num_epochs=10, random_state=123)
params = {'debias': [True, False]}
clf = GridSearchCV(adv_deb, params, cv=3)
clf.fit(X, y)
clf.best_estimator_.sess_.close()
assert clf.best_params_ == {'debias': False}
def test_adv_debias_reproduce():
"""Test that the new AdversarialDebiasing is reproducible."""
adv_deb = AdversarialDebiasing('sex', num_epochs=5, random_state=123)
new_preds = adv_deb.fit(X, y).predict(X)
adv_deb.sess_.close()
new_acc = accuracy_score(y, new_preds)
adv_deb2 = AdversarialDebiasing('sex', num_epochs=5, random_state=123)
new_preds = adv_deb2.fit(X, y).predict(X)
adv_deb.sess_.close()
assert new_acc == accuracy_score(y, new_preds)
def test_adv_debias_intersection():
"""Test that the new AdversarialDebiasing runs with >2 protected groups."""
adv_deb = AdversarialDebiasing(scope_name='intersect', num_epochs=5)
adv_deb.fit(X, y)
adv_deb.sess_.close()
assert adv_deb.adversary_logits_.shape[1] == 4