def test_disparate_impact_remover_np_num(self):
fairness_info = {
"favorable_labels": [1.0],
"protected_attributes": [{"feature": 57, "privileged_groups": [1.0]},],
}
trainable = DisparateImpactRemover(
sensitive_attribute=57
) >> LogisticRegression(max_iter=1000)
train_X = self.creditg_np_num["train_X"]
train_y = self.creditg_np_num["train_y"]
trained = trainable.fit(train_X, train_y)
test_X = self.creditg_np_num["test_X"]
test_y = self.creditg_np_num["test_y"]
disparate_impact_scorer = lale.lib.aif360.disparate_impact(**fairness_info)
impact = disparate_impact_scorer(trained, test_X, test_y)
self.assertTrue(0.9 < impact < 1.1, f"impact {impact}")
def test_disparate_impact_remover_pd_cat_no_redact(self):
fairness_info = self.creditg_pd_cat["fairness_info"]
trainable_remi = DisparateImpactRemover(
**fairness_info, redact=False,
preparation=self.prep_pd_cat) >> LogisticRegression(max_iter=1000)
self._attempt_remi_creditg_pd_cat(fairness_info, trainable_remi, 0.65,
0.75)
def test_disparate_impact_remover_np_num(self):
fairness_info = self.creditg_np_num["fairness_info"]
trainable_orig = LogisticRegression(max_iter=1000)
trainable_remi = DisparateImpactRemover(
**fairness_info) >> trainable_orig
train_X = self.creditg_np_num["train_X"]
train_y = self.creditg_np_num["train_y"]
trained_orig = trainable_orig.fit(train_X, train_y)
trained_remi = trainable_remi.fit(train_X, train_y)
test_X = self.creditg_np_num["test_X"]
test_y = self.creditg_np_num["test_y"]
disparate_impact_scorer = lale.lib.aif360.disparate_impact(
**fairness_info)
impact_orig = disparate_impact_scorer(trained_orig, test_X, test_y)
self.assertTrue(0.6 < impact_orig < 1.0, f"impact_orig {impact_orig}")
impact_remi = disparate_impact_scorer(trained_remi, test_X, test_y)
self.assertTrue(0.8 < impact_remi < 1.0, f"impact_remi {impact_remi}")
def test_disparate_impact_remover_pd_cat(self):
fairness_info = self.creditg_pd_cat["fairness_info"]
trainable_remi = DisparateImpactRemover(
**fairness_info,
preprocessing=self.prep_pd_cat) >> LogisticRegression(
max_iter=1000)
self._attempt_remi_creditg_pd_cat(fairness_info, trainable_remi, 0.78,
0.88)
def test_stacking_pre_estimator_mitigation_base_only(self):
model = StackingClassifier(estimators=[
(
"dir+dtc",
DisparateImpactRemover(
**self.fairness_info) >> DecisionTreeClassifier(),
),
("lr", LogisticRegression()),
])
self._attempt_fit_predict(model)
def test_stacking_pre_estimator_mitigation_final_only(self):
model = StackingClassifier(
estimators=[
("dtc", DecisionTreeClassifier()),
("lr", LogisticRegression()),
],
final_estimator=DisparateImpactRemover(**self.fairness_info) >>
DecisionTreeClassifier(),
passthrough=True,
)
self._attempt_fit_predict(model)
def test_adaboost_pre_estimator_mitigation_base(self):
model = AdaBoostClassifier(base_estimator=DisparateImpactRemover(
**self.fairness_info) >> DecisionTreeClassifier())
self._attempt_fit_predict(model)
def test_bagging_pre_estimator_mitigation_ensemble(self):
model = DisparateImpactRemover(
**self.fairness_info) >> BaggingClassifier(
base_estimator=DecisionTreeClassifier())
self._attempt_fit_predict(model)
def test_stacking_pre_estimator_mitigation_ensemble(self):
model = DisparateImpactRemover(
**self.fairness_info) >> StackingClassifier(estimators=[(
"dtc", DecisionTreeClassifier()), ("lr",
LogisticRegression())])
self._attempt_fit_predict(model)