def test_pipeline_freeze_trained(self):
from lale.lib.sklearn import MinMaxScaler, LogisticRegression
trainable = MinMaxScaler() >> LogisticRegression()
X = [[0.0], [1.0], [2.0]]
y = [0.0, 0.0, 1.0]
liquid = trainable.fit(X, y)
frozen = liquid.freeze_trained()
self.assertFalse(liquid.is_frozen_trained())
self.assertTrue(frozen.is_frozen_trained())
def test_trained_pipeline_freeze_trainable(self):
from lale.lib.sklearn import MinMaxScaler, LogisticRegression
from lale.operators import TrainedPipeline
trainable = MinMaxScaler() >> LogisticRegression()
X = [[0.0], [1.0], [2.0]]
y = [0.0, 0.0, 1.0]
liquid = trainable.fit(X, y)
self.assertIsInstance(liquid, TrainedPipeline)
self.assertFalse(liquid.is_frozen_trainable())
frozen = liquid.freeze_trainable()
self.assertFalse(liquid.is_frozen_trainable())
self.assertTrue(frozen.is_frozen_trainable())
self.assertIsInstance(frozen, TrainedPipeline)
def test_get_named_pipeline(self):
from lale.lib.lale import Hyperopt, OptimizeLast
pipeline = MinMaxScaler() >> KNeighborsClassifier()
trained_pipeline = pipeline.fit(self.X_train, self.y_train)
hyperopt_args = {"cv": 3, "max_evals": 2}
opt_last = OptimizeLast(
estimator=trained_pipeline,
last_optimizer=Hyperopt,
optimizer_args=hyperopt_args,
)
res_last = opt_last.fit(self.X_train, self.y_train)
pipeline2 = res_last.get_pipeline(pipeline_name="p1")
if pipeline2 is not None:
trained_pipeline2 = pipeline2.fit(self.X_train, self.y_train)
_ = trained_pipeline2.predict(self.X_test)
self.assertEqual(type(trained_pipeline), type(trained_pipeline2))