def test_with_hyperopt(self): def my_scorer(estimator, X, y=None): return 1 from lale.lib.lale import Hyperopt hyperopt = Hyperopt( estimator=IsolationForest(max_features=1.0, max_samples=1.0), max_evals=5, verbose=True, scoring=my_scorer, ) trained = hyperopt.fit(self.X_train) _ = trained.predict(self.X_test)
def test_decision_function_1(self): def my_scorer(estimator, X, y=None): return 1 from lale.lib.lale import Hyperopt hyperopt = Hyperopt( estimator=IsolationForest(max_features=1.0, max_samples=1.0), max_evals=5, verbose=True, scoring=my_scorer, ) trained = hyperopt.fit(self.X_train) pipeline = trained.get_pipeline() assert pipeline is not None _ = pipeline.decision_function(self.X_test)
def test_with_incompatible_estimator_1(self): trainable_pipeline = IsolationForest() trained_pipeline = trainable_pipeline.fit(self.X_train, self.y_train) with self.assertRaises(AttributeError): _ = trained_pipeline.predict_log_proba(self.X_test)
def test_trainable_pipeline(self): trainable_pipeline = StandardScaler() >> IsolationForest() trainable_pipeline.fit(self.X_train, self.y_train) with self.assertWarns(DeprecationWarning): _ = trainable_pipeline.score_samples(self.X_test)
def test_trained_pipeline(self): trainable_pipeline = StandardScaler() >> IsolationForest() trained_pipeline = trainable_pipeline.fit(self.X_train, self.y_train) _ = trained_pipeline.score_samples(self.X_test)
def test_with_no_y(self): clf = IsolationForest() trained = clf.fit(self.X_train) trained.predict(self.X_test)
def test_score_samples_trained_trainable(self): clf = IsolationForest() clf.fit(self.X_train) with self.assertWarns(DeprecationWarning): clf.score_samples(self.X_test)
def test_score_samples_trainable(self): clf = IsolationForest() with self.assertRaises(ValueError): clf.score_samples(self.X_test)
def test_score_samples(self): clf = IsolationForest() trained = clf.fit(self.X_train) trained.score_samples(self.X_test)