def test_first_call(self):
trainable_pipeline = StandardScaler()
trained_pipeline = trainable_pipeline.fit(self.X_train, self.y_train)
new_pipeline = trained_pipeline.freeze_trained() >> SGDClassifier()
new_trained_pipeline = new_pipeline.partial_fit(self.X_train,
self.y_train,
classes=[0, 1, 2])
_ = new_trained_pipeline.predict(self.X_test)
def test_batching_with_hyperopt(self):
from lale.lib.lale import Batching, Hyperopt
from lale.lib.sklearn import MinMaxScaler, SGDClassifier
pipeline = Batching(operator=MinMaxScaler() >> SGDClassifier())
trained = pipeline.auto_configure(
self.X_train, self.y_train, optimizer=Hyperopt, max_evals=1
)
_ = trained.predict(self.X_test)
def test_batching_with_hyperopt(self):
from lale.lib.sklearn import MinMaxScaler, SGDClassifier
from lale.lib.lale import Hyperopt, Batching
from sklearn.metrics import accuracy_score
pipeline = Batching(operator=MinMaxScaler() >> SGDClassifier())
trained = pipeline.auto_configure(self.X_train,
self.y_train,
optimizer=Hyperopt,
max_evals=1)
predictions = trained.predict(self.X_test)
def test_call_on_trainable(self):
trainable_pipeline = StandardScaler()
trained_pipeline = trainable_pipeline.fit(self.X_train, self.y_train)
new_pipeline = trained_pipeline.freeze_trained() >> SGDClassifier()
new_pipeline.partial_fit(self.X_train, self.y_train, classes=[0, 1, 2])
new_pipeline.pretty_print()
new_trained_pipeline = new_pipeline.partial_fit(self.X_test,
self.y_test,
classes=[0, 1, 2])
self.assertEqual(new_trained_pipeline, new_pipeline._trained)
_ = new_trained_pipeline.predict(self.X_test)
new_pipeline.partial_fit(self.X_train, self.y_train, classes=[0, 1, 2])
def test_second_call_with_different_classes_trainable(self):
trainable_pipeline = StandardScaler()
trained_pipeline = trainable_pipeline.fit(self.X_train, self.y_train)
new_pipeline = trained_pipeline.freeze_trained() >> SGDClassifier()
new_pipeline.partial_fit(self.X_train, self.y_train, classes=[0, 1, 2])
# Once SGDClassifier is trained, it has a classes_ attribute.
self.assertTrue(self._last_impl_has(new_pipeline._trained, "classes_"))
subset_labels = self.y_test[np.where(self.y_test != 0)]
subset_X = self.X_test[0:len(subset_labels)]
new_trained_pipeline = new_pipeline.partial_fit(
subset_X, subset_labels)
_ = new_trained_pipeline.predict(self.X_test)
def test_second_call_without_classes(self):
trainable_pipeline = StandardScaler()
trained_pipeline = trainable_pipeline.fit(self.X_train, self.y_train)
new_pipeline = trained_pipeline.freeze_trained() >> SGDClassifier()
new_trained_pipeline = new_pipeline.partial_fit(self.X_train,
self.y_train,
classes=[0, 1, 2])
# Once SGDClassifier is trained, it has a classes_ attribute.
self.assertTrue(self._last_impl_has(new_trained_pipeline, "classes_"))
new_trained_pipeline = new_trained_pipeline.partial_fit(
self.X_test, self.y_test)
_ = new_trained_pipeline.predict(self.X_test)
def test_sgd_classifier_3(self):
from lale.lib.sklearn import SGDClassifier
reg = SGDClassifier(l1_ratio=0.2, penalty='l1')
reg.fit(self.X_train, self.y_train)
def test_sgd_classifier_2(self):
from lale.lib.sklearn import SGDClassifier
reg = SGDClassifier(early_stopping=False, validation_fraction=0.2)
reg.fit(self.X_train, self.y_train)
def test_sgd_classifier_1(self):
from lale.lib.sklearn import SGDClassifier
reg = SGDClassifier(learning_rate='optimal', eta0=0.2)
reg.fit(self.X_train, self.y_train)
def test_sgd_classifier(self):
from lale.lib.sklearn import SGDClassifier
reg = SGDClassifier(loss='squared_loss', epsilon=0.2)
reg.fit(self.X_train, self.y_train)
def test_sgd_classifier_3(self):
reg = SGDClassifier(l1_ratio=0.2, penalty="l1")
reg.fit(self.X_train, self.y_train)
def test_sgd_classifier_2(self):
reg = SGDClassifier(early_stopping=False, validation_fraction=0.2)
reg.fit(self.X_train, self.y_train)
def test_sgd_classifier_1(self):
reg = SGDClassifier(learning_rate="optimal", eta0=0.2)
reg.fit(self.X_train, self.y_train)
def test_sgd_classifier(self):
reg = SGDClassifier(loss="squared_loss", epsilon=0.2)
reg.fit(self.X_train, self.y_train)
def test_export_to_sklearn_pipeline5(self):
lale_pipeline = PCA() >> (XGBClassifier() | SGDClassifier())
with self.assertRaises(ValueError):
_ = lale_pipeline.export_to_sklearn_pipeline()