def test_two_estimators_predict1(self):
pipeline = (
StandardScaler() >>
(PCA() & Nystroem() & PassiveAggressiveClassifier()) >>
ConcatFeatures() >> NoOp() >> PassiveAggressiveClassifier())
trained = pipeline.fit(self.X_train, self.y_train)
trained.predict(self.X_test)
def test_remove_last2(self):
pipeline = (StandardScaler() >>
(PCA() & Nystroem() & PassiveAggressiveClassifier()) >>
ConcatFeatures() >> NoOp() >>
(PassiveAggressiveClassifier() & LogisticRegression()))
with self.assertRaises(ValueError):
pipeline.remove_last()
def test_remove_last4(self):
pipeline = StandardScaler() >> (
PCA() & Nystroem() & PassiveAggressiveClassifier()
) >> ConcatFeatures() >> NoOp() >> PassiveAggressiveClassifier()
new_pipeline = pipeline.remove_last(inplace=True)
self.assertEqual(len(new_pipeline._steps), 6)
self.assertEqual(len(pipeline._steps), 6)
def test_two_estimators_predict_proba1(self):
pipeline = StandardScaler() >> (
PCA() & Nystroem() & PassiveAggressiveClassifier()
) >> ConcatFeatures() >> NoOp() >> PassiveAggressiveClassifier()
pipeline.fit(self.X_train, self.y_train)
with self.assertRaises(ValueError):
pipeline.predict_proba(self.X_test)
def test_remove_last5(self):
pipeline = (
StandardScaler()
>> (PCA() & Nystroem() & PassiveAggressiveClassifier())
>> ConcatFeatures()
>> NoOp()
>> PassiveAggressiveClassifier()
)
pipeline.remove_last(inplace=True).freeze_trainable()
def test_passive_aggressive_classifier(self):
from lale.lib.sklearn import PassiveAggressiveClassifier
reg = PassiveAggressiveClassifier(validation_fraction=0.4, early_stopping=False)
reg.fit(self.X_train, self.y_train)
def test_passive_aggressive_classifier(self):
reg = PassiveAggressiveClassifier(validation_fraction=0.4,
early_stopping=False)
reg.fit(self.X_train, self.y_train)
