def test_disable_schema_validation_individual_op(self):
os.environ["LALE_DISABLE_SCHEMA_VALIDATION"] = "True"
import lale.schemas as schemas
from lale.lib.sklearn import PCA
pca_input = schemas.Object(
X=schemas.AnyOf(
[
schemas.Array(schemas.Array(schemas.String())),
schemas.Array(schemas.String()),
]
)
)
foo = PCA.customize_schema(input_fit=pca_input)
pca_output = schemas.Object(
X=schemas.AnyOf(
[
schemas.Array(schemas.Array(schemas.String())),
schemas.Array(schemas.String()),
]
)
)
foo = foo.customize_schema(output_transform=pca_output)
abc = foo()
trained_pca = abc.fit(self.X_train)
trained_pca.transform(self.X_test)
os.environ["LALE_DISABLE_SCHEMA_VALIDATION"] = "False"
def test_enable_schema_validation_individual_op(self):
existing_flag = disable_data_schema_validation
set_disable_data_schema_validation(False)
import lale.schemas as schemas
from lale.lib.sklearn import PCA
pca_input = schemas.Object(X=schemas.AnyOf([
schemas.Array(schemas.Array(schemas.String())),
schemas.Array(schemas.String()),
]))
foo = PCA.customize_schema(input_fit=pca_input)
pca_output = schemas.Object(X=schemas.AnyOf([
schemas.Array(schemas.Array(schemas.String())),
schemas.Array(schemas.String()),
]))
foo = foo.customize_schema(output_transform=pca_output)
abc = foo()
with self.assertRaises(ValueError):
trained_pca = abc.fit(self.X_train)
trained_pca.transform(self.X_test)
set_disable_data_schema_validation(existing_flag)
def test_disable_schema_validation_pipeline(self):
os.environ["LALE_DISABLE_SCHEMA_VALIDATION"]='True'
from lale.lib.sklearn import PCA, LogisticRegression
import lale.schemas as schemas
lr_input = schemas.Object(required=['X', 'y'], X=schemas.AnyOf([
schemas.Array(
schemas.Array(
schemas.String())),
schemas.Array(
schemas.String())]),
y=schemas.Array(schemas.String()))
foo = LogisticRegression.customize_schema(input_fit=lr_input)
abc = foo()
pipeline = PCA() >> abc
trained_pipeline = pipeline.fit(self.X_train, self.y_train)
trained_pipeline.predict(self.X_test)
os.environ["LALE_DISABLE_SCHEMA_VALIDATION"]='False'
def test_enable_schema_validation_pipeline(self):
with EnableSchemaValidation():
import lale.schemas as schemas
from lale.lib.sklearn import PCA, LogisticRegression
lr_input = schemas.Object(
required=["X", "y"],
X=schemas.AnyOf([
schemas.Array(schemas.Array(schemas.String())),
schemas.Array(schemas.String()),
]),
y=schemas.Array(schemas.String()),
)
foo = LogisticRegression.customize_schema(input_fit=lr_input)
abc = foo()
pipeline = PCA() >> abc
with self.assertRaises(ValueError):
trained_pipeline = pipeline.fit(self.X_train, self.y_train)
trained_pipeline.predict(self.X_test)
def test_disable_schema_validation_pipeline(self):
existing_flag = disable_data_schema_validation
set_disable_data_schema_validation(True)
import lale.schemas as schemas
from lale.lib.sklearn import PCA, LogisticRegression
lr_input = schemas.Object(
required=["X", "y"],
X=schemas.AnyOf([
schemas.Array(schemas.Array(schemas.String())),
schemas.Array(schemas.String()),
]),
y=schemas.Array(schemas.String()),
)
foo = LogisticRegression.customize_schema(input_fit=lr_input)
abc = foo()
pipeline = PCA() >> abc
trained_pipeline = pipeline.fit(self.X_train, self.y_train)
trained_pipeline.predict(self.X_test)
set_disable_data_schema_validation(existing_flag)
