def test_handle_unknown_error(self):
from lale.lib.sklearn import OrdinalEncoder
fproc_oe = OrdinalEncoder(handle_unknown="error")
# test_init_fit_transform
trained_oe = fproc_oe.fit(self.X_train, self.y_train)
with self.assertRaises(
ValueError
): # This is repying on the train_test_split, so may fail randomly
_ = trained_oe.transform(self.X_test)
def test_encode_unknown_with(self):
from lale.lib.sklearn import OrdinalEncoder
fproc_oe = OrdinalEncoder(handle_unknown="ignore", encode_unknown_with=1000)
# test_init_fit_transform
trained_oe = fproc_oe.fit(self.X_train, self.y_train)
transformed_X = trained_oe.transform(self.X_test)
# This is repying on the train_test_split, so may fail randomly
self.assertTrue(1000 in transformed_X)
# Testing that inverse_transform works even for encode_unknown_with=1000
_ = trained_oe._impl.inverse_transform(transformed_X)
def test_inverse_transform(self):
from lale.lib.sklearn import OneHotEncoder, OrdinalEncoder
fproc_ohe = OneHotEncoder(handle_unknown="ignore")
# test_init_fit_transform
trained_ohe = fproc_ohe.fit(self.X_train, self.y_train)
transformed_X = trained_ohe.transform(self.X_test)
orig_X_ohe = trained_ohe._impl._wrapped_model.inverse_transform(transformed_X)
fproc_oe = OrdinalEncoder(handle_unknown="ignore")
# test_init_fit_transform
trained_oe = fproc_oe.fit(self.X_train, self.y_train)
transformed_X = trained_oe.transform(self.X_test)
orig_X_oe = trained_oe._impl.inverse_transform(transformed_X)
self.assertEqual(orig_X_ohe.all(), orig_X_oe.all())
def test_with_hyperopt(self):
from lale.lib.sklearn import OrdinalEncoder
X_train, y_train = self.X_train, self.y_train
X_test, y_test = self.X_test, self.y_test
fproc = OrdinalEncoder()
from lale.lib.sklearn import LogisticRegression
pipeline = fproc >> LogisticRegression()
#Tune the pipeline with LR using Hyperopt
from lale.lib.lale import Hyperopt
hyperopt = Hyperopt(estimator=pipeline, max_evals=1)
trained = hyperopt.fit(self.X_train, self.y_train)
predictions = trained.predict(self.X_test)
def test_with_hyperopt(self):
from lale.lib.sklearn import OrdinalEncoder
fproc = OrdinalEncoder(handle_unknown="ignore")
from lale.lib.sklearn import LogisticRegression
pipeline = fproc >> LogisticRegression()
# Tune the pipeline with LR using Hyperopt
from lale.lib.lale import Hyperopt
hyperopt = Hyperopt(estimator=pipeline, max_evals=1)
trained = hyperopt.fit(self.X_train, self.y_train)
_ = trained.predict(self.X_test)
