def _GetAllowedDefaultValue(
value_type: pa.DataType,
default_value_proto: schema_pb2.TensorRepresentation.DefaultValue
) -> Union[int, float, bytes]:
"""Returns the default value set in DefaultValue proto or raises."""
kind = default_value_proto.WhichOneof("kind")
if kind in ("int_value", "uint_value") and pa.types.is_integer(value_type):
value = getattr(default_value_proto, kind)
iinfo = np.iinfo(value_type.to_pandas_dtype())
if value <= iinfo.max and value >= iinfo.min:
return value
else:
raise ValueError("Integer default value out of range: {} is set for a "
"{} column".format(value, value_type))
elif kind == "float_value" and pa.types.is_floating(value_type):
return default_value_proto.float_value
elif kind == "bytes_value" and _IsBinaryLike(value_type):
return default_value_proto.bytes_value
raise ValueError(
"Incompatible default value: {} is set for a {} column".format(
kind, value_type))
def _GetDefaultValuesList(
unbatched_shape: List[int], feature_type: schema_pb2.FeatureType,
default_value_proto: schema_pb2.TensorRepresentation.DefaultValue
) -> List[Union[int, float, bytes]]:
"""Returns a List filled with the default value given in the proto.
Args:
unbatched_shape: The shape of the tensor to fill.
feature_type: The expected type of the default_value.
default_value_proto: The DefaultValue proto that holds the default_value.
Raises:
ValueError: if the default_value is incompatible with feature_type.
"""
kind = default_value_proto.WhichOneof("kind")
default_value = getattr(default_value_proto, kind)
expected_feature_type = _DEFAULT_VALUE_KIND_TO_FEATURE_TYPE.get(kind, None)
if feature_type != expected_feature_type:
raise ValueError(
"FeatureType: {} is incompatible with default_value: {}".format(
schema_pb2.FeatureType.Name(feature_type), default_value))
size = int(np.prod(unbatched_shape, initial=1))
return [default_value] * size