def test_override_float_param2(self):
from lale.lib.sklearn import PCA
from lale.search.schema2search_space import op_to_search_space
from lale.search.search_space import SearchSpaceNumber, SearchSpaceObject
pca = PCA.customize_schema(
relevantToOptimizer=["tol"],
tol=schemas.Float(
minimum=0,
minimumForOptimizer=0.25,
maximum=1.5,
maximumForOptimizer=1.0,
exclusiveMaximumForOptimizer=False,
exclusiveMinimumForOptimizer=True,
),
)
search = op_to_search_space(pca)
assert isinstance(search, SearchSpaceObject)
num_space = list(search.choices)[0][0]
assert isinstance(num_space, SearchSpaceNumber)
self.assertEqual(num_space.minimum, 0.25)
self.assertEqual(num_space.maximum, 1.0)
self.assertFalse(num_space.exclusiveMaximum)
self.assertTrue(num_space.exclusiveMinimum)
def test_disable_schema_validation_individual_op(self):
existing_flag = disable_data_schema_validation
set_disable_data_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)
set_disable_data_schema_validation(existing_flag)