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)