def test_ada_boost1(self):
from sklearn.tree import DecisionTreeClassifier
from lale.lib.sklearn import AdaBoostClassifier
clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier())
clf.fit(self.X_train, self.y_train)
def test_ada_boost(self):
from lale.lib.sklearn import AdaBoostClassifier, DecisionTreeClassifier
clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier())
trained = clf.auto_configure(
self.X_train, self.y_train, optimizer=Hyperopt, max_evals=1
)
# Checking that the inner decision tree does not get the default value for min_samples_leaf, not sure if this will always pass
self.assertNotEqual(
trained.hyperparams()["base_estimator"].hyperparams()["min_samples_leaf"], 1
)
def test_adaboost_post_estimator_mitigation_base(self):
model = AdaBoostClassifier(
base_estimator=CalibratedEqOddsPostprocessing(
**self.fairness_info, estimator=DecisionTreeClassifier()
)
)
self._attempt_fit_predict(model)
def test_trainable_pipeline(self):
trainable_pipeline = StandardScaler() >> AdaBoostClassifier()
trainable_pipeline.fit(self.X_train, self.y_train)
with self.assertWarns(DeprecationWarning):
_ = trainable_pipeline.predict_log_proba(self.X_test)
def test_trained_pipeline(self):
trainable_pipeline = StandardScaler() >> AdaBoostClassifier()
trained_pipeline = trainable_pipeline.fit(self.X_train, self.y_train)
_ = trained_pipeline.predict_log_proba(self.X_test)
def test_adaboost_pre_estimator_mitigation_base(self):
model = AdaBoostClassifier(base_estimator=DisparateImpactRemover(
**self.fairness_info) >> DecisionTreeClassifier())
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)