def test_stacking_in_estimator_mitigation_base_and_final(self):
model = StackingClassifier(
estimators=[
("pr", PrejudiceRemover(**self.fairness_info)),
("lr", LogisticRegression()),
],
final_estimator=PrejudiceRemover(**self.fairness_info),
passthrough=True,
)
self._attempt_fit_predict(model)
def test_stacking_in_estimator_mitigation_base_only(self):
model = StackingClassifier(
estimators=[
("pr", PrejudiceRemover(**self.fairness_info)),
("lr", LogisticRegression()),
]
)
self._attempt_fit_predict(model)
def test_prejudice_remover_pd_cat(self):
fairness_info = self.creditg_pd_cat["fairness_info"]
trainable_remi = PrejudiceRemover(
**fairness_info, preprocessing=self.prep_pd_cat
)
self._attempt_remi_creditg_pd_cat(fairness_info, trainable_remi, 0.70, 0.80)
def test_prejudice_remover_pd_num(self):
fairness_info = self.creditg_pd_num["fairness_info"]
trainable_remi = PrejudiceRemover(**fairness_info)
self._attempt_remi_creditg_pd_num(fairness_info, trainable_remi, 0.73, 0.83)
def test_bagging_in_estimator_mitigation_base(self):
model = BaggingClassifier(base_estimator=PrejudiceRemover(
**self.fairness_info))
self._attempt_fit_predict(model)
def test_adaboost_in_estimator_mitigation_base(self):
model = AdaBoostClassifier(base_estimator=PrejudiceRemover(
**self.fairness_info))
self._attempt_fit_predict(model)