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)