Transformation Function#
TransformationFunction#
hsfs.transformation_function.TransformationFunction(
featurestore_id,
transformation_fn=None,
version=None,
name=None,
source_code_content=None,
builtin_source_code=None,
output_type=None,
id=None,
type=None,
items=None,
count=None,
href=None,
)
Properties#
id#
Training dataset id.
name#
output_type#
source_code_content#
transformation_fn#
transformer_code#
version#
Methods#
delete#
TransformationFunction.delete()
Delete transformation function from backend.
save#
TransformationFunction.save()
Persist transformation function in backend.
Creation#
create_transformation_function#
FeatureStore.create_transformation_function(transformation_function, output_type, version=None)
Create a transformation function metadata object.
Lazy
This method is lazy and does not persist the transformation function in the
feature store on its own. To materialize the transformation function and save
call the save()
method of the transformation function metadata object.
Arguments
- transformation_function
callable
: callable object. - output_type
Union[str, bytes, int, numpy.int8, numpy.int16, numpy.int32, numpy.int64, float, numpy.float64, datetime.datetime, numpy.datetime64, datetime.date, bool]
: python or numpy output type that will be inferred as pyspark.sql.types type.
Returns:
TransformationFunction
: The TransformationFunction metadata object.
Retrieval#
get_transformation_function#
FeatureStore.get_transformation_function(name, version=None)
Get transformation function metadata object.
Arguments
- name
str
: name of transformation function. - version
Optional[int]
: version of transformation function. Optional, if not provided all functions that match to provided name will be retrieved .
Returns:
TransformationFunction
: The TransformationFunction metadata object.
get_transformation_functions#
FeatureStore.get_transformation_functions()
Get all transformation functions metadata objects.
Returns:
List[TransformationFunction]
. List of transformation function instances.