snowflake.ml.model.model_signature.FeatureSpec¶

class snowflake.ml.model.model_signature.FeatureSpec(name: str, dtype: DataType, shape: tuple[int, ...] | None = None, nullable: bool = True)¶

Bases: BaseFeatureSpec

Specification of a feature in Snowflake native model packaging.

Initialize a feature.

Parameters:

• name – Name of the feature.

• dtype – Type of the elements in the feature.

• nullable – Whether the feature is nullable. Defaults to True.

• shape –

  Used to represent scalar feature, 1-d feature list, or n-d tensor. Use -1 to represent variable length. Defaults to None.

  Examples

  • None: scalar

  • (2,): 1d list with a fixed length of 2.

  • (-1,): 1d list with variable length, used for ragged tensor representation.

  • (d1, d2, d3): 3d tensor.

• nullable – Whether the feature is nullable. Defaults to True.

Raises:

• SnowflakeMLException – TypeError: When the dtype input type is incorrect.

• SnowflakeMLException – TypeError: When the shape input type is incorrect.

Methods

as_dtype(force_numpy_dtype: bool = False) → type[Any] | dtype[Any] | _SupportsDType[dtype[Any]] | tuple[Any, Any] | list[Any] | _DTypeDict | str | None | Int8Dtype | Int16Dtype | Int32Dtype | Int64Dtype | UInt8Dtype | UInt16Dtype | UInt32Dtype | UInt64Dtype | Float32Dtype | Float64Dtype | BooleanDtype | StringDtype¶

Convert to corresponding local Type.

as_snowpark_type() → DataType¶

Convert to corresponding Snowpark Type.

classmethod from_dict(input_dict: dict[str, Any]) → FeatureSpec¶

Deserialize the feature specification from a dict.

Parameters:

input_dict – The dict containing information of the feature specification.

Returns:

A feature specification instance deserialized and created from the dict.

classmethod from_mlflow_spec(input_spec: mlflow.types.ColSpec | mlflow.types.TensorSpec, feature_name: str) → BaseFeatureSpec¶

to_dict() → dict[str, Any]¶

Serialize the feature group into a dict.

Returns:

A dict that serializes the feature group.

Attributes

name¶

Name of the feature.