def test_calibrated_eq_odds_postprocessing_pd_cat(self):
fairness_info = self.creditg_pd_cat["fairness_info"]
estim = self.prep_pd_cat >> LogisticRegression(max_iter=1000)
trainable_remi = CalibratedEqOddsPostprocessing(
**fairness_info, estimator=estim
)
self._attempt_remi_creditg_pd_cat(fairness_info, trainable_remi, 0.65, 0.85)
def test_stacking_post_estimator_mitigation_base_and_final(self):
model = StackingClassifier(
estimators=[
(
"dtc+ceop",
CalibratedEqOddsPostprocessing(
**self.fairness_info,
estimator=DecisionTreeClassifier()),
),
("lr", LogisticRegression()),
],
final_estimator=CalibratedEqOddsPostprocessing(
**self.fairness_info, estimator=DecisionTreeClassifier()),
passthrough=True,
)
self._attempt_fit_predict(model)
def test_bagging_post_estimator_mitigation_base(self):
model = BaggingClassifier(
base_estimator=CalibratedEqOddsPostprocessing(
**self.fairness_info, estimator=DecisionTreeClassifier()
)
)
self._attempt_fit_predict(model)
def test_stacking_post_estimator_mitigation_ensemble(self):
model = CalibratedEqOddsPostprocessing(
**self.fairness_info,
estimator=StackingClassifier(estimators=[
("dtc", DecisionTreeClassifier()),
("lr", LogisticRegression()),
]))
self._attempt_fit_predict(model)
def test_voting_post_estimator_mitigation_base(self):
model = VotingClassifier(estimators=[
(
"dtc+ceop",
CalibratedEqOddsPostprocessing(
**self.fairness_info, estimator=DecisionTreeClassifier()),
),
("lr", LogisticRegression()),
])
self._attempt_fit_predict(model)
def test_adaboost_post_estimator_mitigation_ensemble(self):
model = CalibratedEqOddsPostprocessing(
**self.fairness_info,
estimator=AdaBoostClassifier(
base_estimator=DecisionTreeClassifier()))
self._attempt_fit_predict(model)
