Source code for mlflow.types.schema

import builtins
import datetime as dt
import importlib.util
import json
import string
import warnings
from copy import deepcopy
from dataclasses import is_dataclass
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, TypedDict, Union, get_args, get_origin

import numpy as np

from mlflow.exceptions import MlflowException
from mlflow.utils.annotations import experimental

ARRAY_TYPE = "array"
OBJECT_TYPE = "object"
MAP_TYPE = "map"
SPARKML_VECTOR_TYPE = "sparkml_vector"


[docs]class DataType(Enum): """ MLflow data types. """ def __new__(cls, value, numpy_type, spark_type, pandas_type=None, python_type=None): res = object.__new__(cls) res._value_ = value res._numpy_type = numpy_type res._spark_type = spark_type res._pandas_type = pandas_type if pandas_type is not None else numpy_type res._python_type = python_type if python_type is not None else numpy_type return res # NB: We only use pandas extension type for strings. There are also pandas extension types for # integers and boolean values. We do not use them here for now as most downstream tools are # most likely to use / expect native numpy types and would not be compatible with the extension # types. boolean = (1, np.dtype("bool"), "BooleanType", np.dtype("bool"), bool) """Logical data (True, False) .""" integer = (2, np.dtype("int32"), "IntegerType", np.dtype("int32"), int) """32b signed integer numbers.""" long = (3, np.dtype("int64"), "LongType", np.dtype("int64"), int) """64b signed integer numbers. """ float = (4, np.dtype("float32"), "FloatType", np.dtype("float32"), builtins.float) """32b floating point numbers. """ double = (5, np.dtype("float64"), "DoubleType", np.dtype("float64"), builtins.float) """64b floating point numbers. """ string = (6, np.dtype("str"), "StringType", object, str) """Text data.""" binary = (7, np.dtype("bytes"), "BinaryType", object, bytes) """Sequence of raw bytes.""" datetime = ( 8, np.dtype("datetime64[ns]"), "TimestampType", np.dtype("datetime64[ns]"), dt.date, ) """64b datetime data.""" def __repr__(self): return self.name
[docs] def to_numpy(self) -> np.dtype: """Get equivalent numpy data type.""" return self._numpy_type
[docs] def to_pandas(self) -> np.dtype: """Get equivalent pandas data type.""" return self._pandas_type
def to_spark(self): if self._spark_type == "VectorUDT": from pyspark.ml.linalg import VectorUDT return VectorUDT() else: import pyspark.sql.types return getattr(pyspark.sql.types, self._spark_type)()
[docs] def to_python(self): """Get equivalent python data type.""" return self._python_type
@classmethod def is_boolean(cls, value): return type(value) in DataType.boolean.get_all_types() @classmethod def is_integer(cls, value): return type(value) in DataType.integer.get_all_types() @classmethod def is_long(cls, value): return type(value) in DataType.long.get_all_types() @classmethod def is_float(cls, value): return type(value) in DataType.float.get_all_types() @classmethod def is_double(cls, value): return type(value) in DataType.double.get_all_types() @classmethod def is_string(cls, value): return type(value) in DataType.string.get_all_types() @classmethod def is_binary(cls, value): return type(value) in DataType.binary.get_all_types() @classmethod def is_datetime(cls, value): return type(value) in DataType.datetime.get_all_types() def get_all_types(self): types = [self.to_numpy(), self.to_pandas(), self.to_python()] if importlib.util.find_spec("pyspark") is not None: types.append(self.to_spark()) if self.name == "datetime": types.extend([np.datetime64, dt.datetime]) if self.name == "binary": # This is to support identifying bytearrays as binary data # for pandas DataFrame schema inference types.extend([bytearray]) return types @classmethod def get_spark_types(cls): return [dt.to_spark() for dt in cls._member_map_.values()] @classmethod def from_numpy_type(cls, np_type): return next((v for v in cls._member_map_.values() if v.to_numpy() == np_type), None)
@experimental class Property: """ Specification used to represent a json-convertible object property. """ def __init__( self, name: str, dtype: Union[DataType, "Array", "Object", "Map", str], required: bool = True, ) -> None: """ Args: name: The name of the property dtype: The data type of the property required: Whether this property is required """ if not isinstance(name, str): raise MlflowException.invalid_parameter_value( f"Expected name to be a string, got type {type(name).__name__}" ) self._name = name try: self._dtype = DataType[dtype] if isinstance(dtype, str) else dtype except KeyError: raise MlflowException( f"Unsupported type '{dtype}', expected instance of DataType, Array, Object, Map or " f"one of {[t.name for t in DataType]}" ) if not isinstance(self.dtype, (DataType, Array, Object, Map)): raise MlflowException( "Expected mlflow.types.schema.Datatype, mlflow.types.schema.Array, " "mlflow.types.schema.Object, mlflow.types.schema.Map or str for the 'dtype' " f"argument, but got {self.dtype.__class__}" ) self._required = required @property def name(self) -> str: """The property name.""" return self._name @property def dtype(self) -> Union[DataType, "Array", "Object", "Map"]: """The property data type.""" return self._dtype @property def required(self) -> bool: """Whether this property is required""" return self._required @required.setter def required(self, value: bool) -> None: self._required = value def __eq__(self, other) -> bool: if isinstance(other, Property): return ( self.name == other.name and self.dtype == other.dtype and self.required == other.required ) return False def __lt__(self, other) -> bool: return self.name < other.name def __repr__(self) -> str: required = "required" if self.required else "optional" return f"{self.name}: {self.dtype!r} ({required})" def to_dict(self): d = {"type": self.dtype.name} if isinstance(self.dtype, DataType) else self.dtype.to_dict() d["required"] = self.required return {self.name: d} @classmethod def from_json_dict(cls, **kwargs): """ Deserialize from a json loaded dictionary. The dictionary is expected to contain only one key as `name`, and the value should be a dictionary containing `type` and optional `required` keys. Example: {"property_name": {"type": "string", "required": True}} """ if len(kwargs) != 1: raise MlflowException( f"Expected Property JSON to contain a single key as name, got {len(kwargs)} keys." ) name, dic = kwargs.popitem() if not {"type"} <= set(dic.keys()): raise MlflowException(f"Missing keys in Property `{name}`. Expected to find key `type`") required = dic.pop("required", True) dtype = dic["type"] if dtype == ARRAY_TYPE: return cls(name=name, dtype=Array.from_json_dict(**dic), required=required) if dtype == SPARKML_VECTOR_TYPE: return SparkMLVector() if dtype == OBJECT_TYPE: return cls(name=name, dtype=Object.from_json_dict(**dic), required=required) if dtype == MAP_TYPE: return cls(name=name, dtype=Map.from_json_dict(**dic), required=required) return cls(name=name, dtype=dtype, required=required) def _merge(self, prop: "Property") -> "Property": """ Check if current property is compatible with another property and return the updated property. When two properties have the same name, we need to check if their dtypes are compatible or not. An example of two compatible properties: .. code-block:: python prop1 = Property( name="a", dtype=Object( properties=[Property(name="a", dtype=DataType.string, required=False)] ), ) prop2 = Property( name="a", dtype=Object( properties=[ Property(name="a", dtype=DataType.string), Property(name="b", dtype=DataType.double), ] ), ) merged_prop = prop1._merge(prop2) assert merged_prop == Property( name="a", dtype=Object( properties=[ Property(name="a", dtype=DataType.string, required=False), Property(name="b", dtype=DataType.double, required=False), ] ), ) """ if not isinstance(prop, Property): raise MlflowException( f"Can't merge property with non-property type: {type(prop).__name__}" ) if self.name != prop.name: raise MlflowException("Can't merge properties with different names") required = self.required and prop.required if isinstance(self.dtype, DataType) and isinstance(prop.dtype, DataType): if self.dtype == prop.dtype: return Property(name=self.name, dtype=self.dtype, required=required) raise MlflowException(f"Properties are incompatible for {self.dtype} and {prop.dtype}") if ( isinstance(self.dtype, (Array, Object, Map)) and self.dtype.__class__ is prop.dtype.__class__ ): obj = self.dtype._merge(prop.dtype) return Property(name=self.name, dtype=obj, required=required) raise MlflowException("Properties are incompatible") @experimental class Object: """ Specification used to represent a json-convertible object. """ def __init__(self, properties: List[Property]) -> None: self._check_properties(properties) # Sort by name to make sure the order is stable self._properties = sorted(properties) def _check_properties(self, properties): if not isinstance(properties, list): raise MlflowException.invalid_parameter_value( f"Expected properties to be a list, got type {type(properties).__name__}" ) if len(properties) == 0: raise MlflowException.invalid_parameter_value( "Creating Object with empty properties is not allowed." ) if any(not isinstance(v, Property) for v in properties): raise MlflowException.invalid_parameter_value( "Expected values to be instance of Property" ) # check duplicated property names names = [prop.name for prop in properties] duplicates = {name for name in names if names.count(name) > 1} if len(duplicates) > 0: raise MlflowException.invalid_parameter_value( f"Found duplicated property names: {duplicates}" ) @property def properties(self) -> List[Property]: """The list of object properties""" return self._properties @properties.setter def properties(self, value: List[Property]) -> None: self._check_properties(value) self._properties = sorted(value) def __eq__(self, other) -> bool: if isinstance(other, Object): return self.properties == other.properties return False def __repr__(self) -> str: joined = ", ".join(map(repr, self.properties)) return "{" + joined + "}" def to_dict(self): properties = { name: value for prop in self.properties for name, value in prop.to_dict().items() } return { "type": OBJECT_TYPE, "properties": properties, } @classmethod def from_json_dict(cls, **kwargs): """ Deserialize from a json loaded dictionary. The dictionary is expected to contain `type` and `properties` keys. Example: {"type": "object", "properties": {"property_name": {"type": "string"}}} """ if not {"properties", "type"} <= set(kwargs.keys()): raise MlflowException( "Missing keys in Object JSON. Expected to find keys `properties` and `type`" ) if kwargs["type"] != OBJECT_TYPE: raise MlflowException("Type mismatch, Object expects `object` as the type") if not isinstance(kwargs["properties"], dict) or any( not isinstance(prop, dict) for prop in kwargs["properties"].values() ): raise MlflowException("Expected properties to be a dictionary of Property JSON") return cls( [Property.from_json_dict(**{name: prop}) for name, prop in kwargs["properties"].items()] ) def _merge(self, obj: "Object") -> "Object": """ Check if the current object is compatible with another object and return the updated object. When we infer the signature from a list of objects, it is possible that one object has more properties than the other. In this case, we should mark those optional properties as required=False. For properties with the same name, we should check the compatibility of two properties and update. An example of two compatible objects: .. code-block:: python obj1 = Object( properties=[ Property(name="a", dtype=DataType.string), Property(name="b", dtype=DataType.double), ] ) obj2 = Object( properties=[ Property(name="a", dtype=DataType.string), Property(name="c", dtype=DataType.boolean), ] ) updated_obj = obj1._merge(obj2) assert updated_obj == Object( properties=[ Property(name="a", dtype=DataType.string), Property(name="b", dtype=DataType.double, required=False), Property(name="c", dtype=DataType.boolean, required=False), ] ) """ if not isinstance(obj, Object): raise MlflowException(f"Can't merge object with non-object type: {type(obj).__name__}") if self == obj: return deepcopy(self) prop_dict1 = {prop.name: prop for prop in self.properties} prop_dict2 = {prop.name: prop for prop in obj.properties} updated_properties = [] # For each property in the first element, if it doesn't appear # later, we update required=False for k in prop_dict1.keys() - prop_dict2.keys(): updated_properties.append(Property(name=k, dtype=prop_dict1[k].dtype, required=False)) # For common keys, property type should be the same for k in prop_dict1.keys() & prop_dict2.keys(): updated_properties.append(prop_dict1[k]._merge(prop_dict2[k])) # For each property appears in the second elements, if it doesn't # exist, we update and set required=False for k in prop_dict2.keys() - prop_dict1.keys(): updated_properties.append(Property(name=k, dtype=prop_dict2[k].dtype, required=False)) return Object(properties=updated_properties) class Array: """ Specification used to represent a json-convertible array. """ def __init__( self, dtype: Union["Array", "Map", DataType, Object, str], ) -> None: try: self._dtype = DataType[dtype] if isinstance(dtype, str) else dtype except KeyError: raise MlflowException( f"Unsupported type '{dtype}', expected instance of DataType, Array, Object, Map or " f"one of {[t.name for t in DataType]}" ) if not isinstance(self.dtype, (Array, DataType, Object, Map)): raise MlflowException( "Expected mlflow.types.schema.Array, mlflow.types.schema.Datatype, " "mlflow.types.schema.Object, mlflow.types.schema.Map or str for the " f"'dtype' argument, but got '{self.dtype.__class__}'" ) @property def dtype(self) -> Union["Array", DataType, Object]: """The array data type.""" return self._dtype def __eq__(self, other) -> bool: if isinstance(other, Array): return self.dtype == other.dtype return False def to_dict(self): items = ( {"type": self.dtype.name} if isinstance(self.dtype, DataType) else self.dtype.to_dict() ) return {"type": ARRAY_TYPE, "items": items} @classmethod def from_json_dict(cls, **kwargs): """ Deserialize from a json loaded dictionary. The dictionary is expected to contain `type` and `items` keys. Example: {"type": "array", "items": "string"} """ if not {"items", "type"} <= set(kwargs.keys()): raise MlflowException( "Missing keys in Array JSON. Expected to find keys `items` and `type`" ) if kwargs["type"] != ARRAY_TYPE: raise MlflowException("Type mismatch, Array expects `array` as the type") if not isinstance(kwargs["items"], dict): raise MlflowException("Expected items to be a dictionary of Object JSON") if not {"type"} <= set(kwargs["items"].keys()): raise MlflowException("Missing keys in Array's items JSON. Expected to find key `type`") if kwargs["items"]["type"] == OBJECT_TYPE: item_type = Object.from_json_dict(**kwargs["items"]) elif kwargs["items"]["type"] == ARRAY_TYPE: item_type = Array.from_json_dict(**kwargs["items"]) elif kwargs["items"]["type"] == SPARKML_VECTOR_TYPE: item_type = SparkMLVector() elif kwargs["items"]["type"] == MAP_TYPE: item_type = Map.from_json_dict(**kwargs["items"]) else: item_type = kwargs["items"]["type"] return cls(dtype=item_type) def __repr__(self) -> str: return f"Array({self.dtype!r})" def _merge(self, arr: "Array") -> "Array": if not isinstance(arr, Array): raise MlflowException(f"Can't merge array with non-array type: {type(arr).__name__}") if self == arr: return deepcopy(self) if isinstance(self.dtype, DataType): if self.dtype == arr.dtype: return Array(dtype=self.dtype) raise MlflowException( f"Array types are incompatible for {self} with dtype={self.dtype} and " f"{arr} with dtype={arr.dtype}" ) if ( isinstance(self.dtype, (Array, Object, Map)) and self.dtype.__class__ is arr.dtype.__class__ ): return Array(dtype=self.dtype._merge(arr.dtype)) raise MlflowException(f"Array type {self!r} and {arr!r} are incompatible") class SparkMLVector(Array): """ Specification used to represent a vector type in Spark ML. """ def __init__(self): super().__init__(dtype=DataType.double) def to_dict(self): return {"type": SPARKML_VECTOR_TYPE} @classmethod def from_json_dict(cls, **kwargs): return SparkMLVector() def __repr__(self) -> str: return "SparkML vector" def __eq__(self, other) -> bool: return isinstance(other, SparkMLVector) def _merge(self, arr: Array) -> Array: if isinstance(arr, SparkMLVector): return deepcopy(self) raise MlflowException("SparkML vector type can't be merged with another Array type.") class Map: """ Specification used to represent a json-convertible map with string type keys. """ def __init__(self, value_type: Union["Array", "Map", DataType, Object, str]): try: self._value_type = DataType[value_type] if isinstance(value_type, str) else value_type except KeyError: raise MlflowException( f"Unsupported value type '{value_type}', expected instance of DataType, Array, " f"Object, Map or one of {[t.name for t in DataType]}" ) if not isinstance(self._value_type, (Array, Map, DataType, Object)): raise MlflowException( "Expected mlflow.types.schema.Array, mlflow.types.schema.Datatype, " "mlflow.types.schema.Object, mlflow.types.schema.Map or str for " f"the 'value_type' argument, but got '{self._value_type}'" ) @property def value_type(self): return self._value_type def __repr__(self) -> str: return f"Map(str -> {self._value_type})" def __eq__(self, other) -> bool: if isinstance(other, Map): return self.value_type == other.value_type return False def to_dict(self): values = ( {"type": self.value_type.name} if isinstance(self.value_type, DataType) else self.value_type.to_dict() ) return {"type": MAP_TYPE, "values": values} @classmethod def from_json_dict(cls, **kwargs): """ Deserialize from a json loaded dictionary. The dictionary is expected to contain `type` and `values` keys. Example: {"type": "map", "values": "string"} """ if not {"values", "type"} <= set(kwargs.keys()): raise MlflowException( "Missing keys in Array JSON. Expected to find keys `items` and `type`" ) if kwargs["type"] != MAP_TYPE: raise MlflowException("Type mismatch, Map expects `map` as the type") if not isinstance(kwargs["values"], dict): raise MlflowException("Expected values to be a dictionary of Object JSON") if not {"type"} <= set(kwargs["values"].keys()): raise MlflowException("Missing keys in Map's items JSON. Expected to find key `type`") if kwargs["values"]["type"] == OBJECT_TYPE: return cls(value_type=Object.from_json_dict(**kwargs["values"])) if kwargs["values"]["type"] == ARRAY_TYPE: return cls(value_type=Array.from_json_dict(**kwargs["values"])) if kwargs["values"]["type"] == SPARKML_VECTOR_TYPE: return SparkMLVector() if kwargs["values"]["type"] == MAP_TYPE: return cls(value_type=Map.from_json_dict(**kwargs["values"])) return cls(value_type=kwargs["values"]["type"]) def _merge(self, map_type: "Map") -> "Map": if not isinstance(map_type, Map): raise MlflowException(f"Can't merge map with non-map type: {type(map_type).__name__}") if self == map_type: return deepcopy(self) if isinstance(self.value_type, DataType): if self.value_type == map_type.value_type: return Map(value_type=self.value_type) raise MlflowException( f"Map types are incompatible for {self} with value_type={self.value_type} and " f"{map_type} with value_type={map_type.value_type}" ) if ( isinstance(self.value_type, (Array, Object, Map)) and self.value_type.__class__ is map_type.value_type.__class__ ): return Map(value_type=self.value_type._merge(map_type.value_type)) raise MlflowException(f"Map type {self!r} and {map_type!r} are incompatible")
[docs]class ColSpec: """ Specification of name and type of a single column in a dataset. """ def __init__( self, type: Union[DataType, Array, Object, str], name: Optional[str] = None, optional: Optional[bool] = None, required: Optional[bool] = None, # TODO: update to required=True after deprecating optional ): self._name = name if optional is not None: if required is not None: raise MlflowException( "Only one of `optional` and `required` can be specified. " "`optional` is deprecated, please use `required` instead." ) else: warnings.warn( "`optional` is deprecated and will be removed in a future version " "of MLflow. Use `required` instead.", category=FutureWarning, ) self._required = not optional else: self._required = True if required is None else required try: self._type = DataType[type] if isinstance(type, str) else type except KeyError: raise MlflowException( f"Unsupported type '{type}', expected instance of DataType or " f"one of {[t.name for t in DataType]}" ) if not isinstance(self.type, (DataType, Array, Object, Map)): raise TypeError( "Expected mlflow.types.schema.Datatype, mlflow.types.schema.Array, " "mlflow.types.schema.Object, mlflow.types.schema.Map or str for the 'type' " f"argument, but got {self.type.__class__}" ) @property def type(self) -> Union[DataType, Array, Object]: """The column data type.""" return self._type @property def name(self) -> Optional[str]: """The column name or None if the columns is unnamed.""" return self._name @name.setter def name(self, value: bool) -> None: self._name = value @experimental @property def optional(self) -> bool: """ Whether this column is optional. .. Warning:: Deprecated. `optional` is deprecated in favor of `required`. """ return not self._required @experimental @property def required(self) -> bool: """Whether this column is required.""" return self._required def to_dict(self) -> Dict[str, Any]: d = {"type": self.type.name} if isinstance(self.type, DataType) else self.type.to_dict() if self.name is not None: d["name"] = self.name d["required"] = self.required return d def __eq__(self, other) -> bool: if isinstance(other, ColSpec): names_eq = (self.name is None and other.name is None) or self.name == other.name return names_eq and self.type == other.type and self.required == other.required return False def __repr__(self) -> str: required = "required" if self.required else "optional" if self.name is None: return f"{self.type!r} ({required})" return f"{self.name!r}: {self.type!r} ({required})"
[docs] @classmethod def from_json_dict(cls, **kwargs): """ Deserialize from a json loaded dictionary. The dictionary is expected to contain `type` and optional `name` and `required` keys. """ if not {"type"} <= set(kwargs.keys()): raise MlflowException("Missing keys in ColSpec JSON. Expected to find key `type`") if kwargs["type"] not in [ARRAY_TYPE, OBJECT_TYPE, MAP_TYPE, SPARKML_VECTOR_TYPE]: return cls(**kwargs) name = kwargs.pop("name", None) optional = kwargs.pop("optional", None) required = kwargs.pop("required", None) if kwargs["type"] == ARRAY_TYPE: return cls( name=name, type=Array.from_json_dict(**kwargs), optional=optional, required=required ) if kwargs["type"] == OBJECT_TYPE: return cls( name=name, type=Object.from_json_dict(**kwargs), optional=optional, required=required, ) if kwargs["type"] == MAP_TYPE: return cls( name=name, type=Map.from_json_dict(**kwargs), optional=optional, required=required ) if kwargs["type"] == SPARKML_VECTOR_TYPE: return cls(name=name, type=SparkMLVector(), optional=optional, required=required)
class TensorInfo: """ Representation of the shape and type of a Tensor. """ def __init__(self, dtype: np.dtype, shape: Union[tuple, list]): if not isinstance(dtype, np.dtype): raise TypeError( f"Expected `dtype` to be instance of `{np.dtype}`, received `{ dtype.__class__}`" ) # Throw if size information exists flexible numpy data types if dtype.char in ["U", "S"] and not dtype.name.isalpha(): raise MlflowException( "MLflow does not support size information in flexible numpy data types. Use" f' np.dtype("{dtype.name.rstrip(string.digits)}") instead' ) if not isinstance(shape, (tuple, list)): raise TypeError( "Expected `shape` to be instance of `{}` or `{}`, received `{}`".format( tuple, list, shape.__class__ ) ) self._dtype = dtype self._shape = tuple(shape) @property def dtype(self) -> np.dtype: """ A unique character code for each of the 21 different numpy built-in types. See https://numpy.org/devdocs/reference/generated/numpy.dtype.html#numpy.dtype for details. """ return self._dtype @property def shape(self) -> tuple: """The tensor shape""" return self._shape def to_dict(self) -> Dict[str, Any]: return {"dtype": self._dtype.name, "shape": self._shape} @classmethod def from_json_dict(cls, **kwargs): """ Deserialize from a json loaded dictionary. The dictionary is expected to contain `dtype` and `shape` keys. """ if not {"dtype", "shape"} <= set(kwargs.keys()): raise MlflowException( "Missing keys in TensorSpec JSON. Expected to find keys `dtype` and `shape`" ) tensor_type = np.dtype(kwargs["dtype"]) tensor_shape = tuple(kwargs["shape"]) return cls(tensor_type, tensor_shape) def __repr__(self) -> str: return f"Tensor({self.dtype.name!r}, {self.shape!r})"
[docs]class TensorSpec: """ Specification used to represent a dataset stored as a Tensor. """ def __init__( self, type: np.dtype, shape: Union[tuple, list], name: Optional[str] = None, ): self._name = name self._tensorInfo = TensorInfo(type, shape) @property def type(self) -> np.dtype: """ A unique character code for each of the 21 different numpy built-in types. See https://numpy.org/devdocs/reference/generated/numpy.dtype.html#numpy.dtype for details. """ return self._tensorInfo.dtype @property def name(self) -> Optional[str]: """The tensor name or None if the tensor is unnamed.""" return self._name @property def shape(self) -> tuple: """The tensor shape""" return self._tensorInfo.shape @experimental @property def required(self) -> bool: """Whether this tensor is required.""" return True def to_dict(self) -> Dict[str, Any]: if self.name is None: return {"type": "tensor", "tensor-spec": self._tensorInfo.to_dict()} else: return {"name": self.name, "type": "tensor", "tensor-spec": self._tensorInfo.to_dict()}
[docs] @classmethod def from_json_dict(cls, **kwargs): """ Deserialize from a json loaded dictionary. The dictionary is expected to contain `type` and `tensor-spec` keys. """ if not {"tensor-spec", "type"} <= set(kwargs.keys()): raise MlflowException( "Missing keys in TensorSpec JSON. Expected to find keys `tensor-spec` and `type`" ) if kwargs["type"] != "tensor": raise MlflowException("Type mismatch, TensorSpec expects `tensor` as the type") tensor_info = TensorInfo.from_json_dict(**kwargs["tensor-spec"]) return cls( tensor_info.dtype, tensor_info.shape, kwargs["name"] if "name" in kwargs else None )
def __eq__(self, other) -> bool: if isinstance(other, TensorSpec): names_eq = (self.name is None and other.name is None) or self.name == other.name return names_eq and self.type == other.type and self.shape == other.shape return False def __repr__(self) -> str: if self.name is None: return repr(self._tensorInfo) else: return f"{self.name!r}: {self._tensorInfo!r}"
[docs]class Schema: """ Specification of a dataset. Schema is represented as a list of :py:class:`ColSpec` or :py:class:`TensorSpec`. A combination of `ColSpec` and `TensorSpec` is not allowed. The dataset represented by a schema can be named, with unique non empty names for every input. In the case of :py:class:`ColSpec`, the dataset columns can be unnamed with implicit integer index defined by their list indices. Combination of named and unnamed data inputs are not allowed. """ def __init__(self, inputs: List[Union[ColSpec, TensorSpec]]): if not isinstance(inputs, list): raise MlflowException.invalid_parameter_value( f"Inputs of Schema must be a list, got type {type(inputs).__name__}" ) if not inputs: raise MlflowException.invalid_parameter_value( "Creating Schema with empty inputs is not allowed." ) if not (all(x.name is None for x in inputs) or all(x.name is not None for x in inputs)): raise MlflowException( "Creating Schema with a combination of named and unnamed inputs " f"is not allowed. Got input names {[x.name for x in inputs]}" ) if not ( all(isinstance(x, TensorSpec) for x in inputs) or all(isinstance(x, ColSpec) for x in inputs) ): raise MlflowException( "Creating Schema with a combination of {0} and {1} is not supported. " "Please choose one of {0} or {1}".format(ColSpec.__class__, TensorSpec.__class__) ) if ( all(isinstance(x, TensorSpec) for x in inputs) and len(inputs) > 1 and any(x.name is None for x in inputs) ): raise MlflowException( "Creating Schema with multiple unnamed TensorSpecs is not supported. " "Please provide names for each TensorSpec." ) if all(x.name is None for x in inputs) and any(x.required is False for x in inputs): raise MlflowException( "Creating Schema with unnamed optional inputs is not supported. " "Please name all inputs or make all inputs required." ) self._inputs = inputs def __len__(self): return len(self._inputs) def __iter__(self): return iter(self._inputs) @property def inputs(self) -> List[Union[ColSpec, TensorSpec]]: """Representation of a dataset that defines this schema.""" return self._inputs
[docs] def is_tensor_spec(self) -> bool: """Return true iff this schema is specified using TensorSpec""" return self.inputs and isinstance(self.inputs[0], TensorSpec)
[docs] def input_names(self) -> List[Union[str, int]]: """Get list of data names or range of indices if the schema has no names.""" return [x.name or i for i, x in enumerate(self.inputs)]
[docs] def required_input_names(self) -> List[Union[str, int]]: """Get list of required data names or range of indices if schema has no names.""" return [x.name or i for i, x in enumerate(self.inputs) if x.required]
[docs] @experimental def optional_input_names(self) -> List[Union[str, int]]: """Get list of optional data names or range of indices if schema has no names.""" return [x.name or i for i, x in enumerate(self.inputs) if not x.required]
[docs] def has_input_names(self) -> bool: """Return true iff this schema declares names, false otherwise.""" return self.inputs and self.inputs[0].name is not None
[docs] def input_types(self) -> List[Union[DataType, np.dtype, Array, Object]]: """Get types for each column in the schema.""" return [x.type for x in self.inputs]
[docs] def input_types_dict(self) -> Dict[str, Union[DataType, np.dtype, Array, Object]]: """Maps column names to types, iff this schema declares names.""" if not self.has_input_names(): raise MlflowException("Cannot get input types as a dict for schema without names.") return {x.name: x.type for x in self.inputs}
[docs] def input_dict(self) -> Dict[str, Union[ColSpec, TensorSpec]]: """Maps column names to inputs, iff this schema declares names.""" if not self.has_input_names(): raise MlflowException("Cannot get input dict for schema without names.") return {x.name: x for x in self.inputs}
[docs] def numpy_types(self) -> List[np.dtype]: """Convenience shortcut to get the datatypes as numpy types.""" if self.is_tensor_spec(): return [x.type for x in self.inputs] if all(isinstance(x.type, DataType) for x in self.inputs): return [x.type.to_numpy() for x in self.inputs] raise MlflowException( "Failed to get numpy types as some of the inputs types are not DataType." )
[docs] def pandas_types(self) -> List[np.dtype]: """Convenience shortcut to get the datatypes as pandas types. Unsupported by TensorSpec.""" if self.is_tensor_spec(): raise MlflowException("TensorSpec only supports numpy types, use numpy_types() instead") if all(isinstance(x.type, DataType) for x in self.inputs): return [x.type.to_pandas() for x in self.inputs] raise MlflowException( "Failed to get pandas types as some of the inputs types are not DataType." )
[docs] def as_spark_schema(self): """Convert to Spark schema. If this schema is a single unnamed column, it is converted directly the corresponding spark data type, otherwise it's returned as a struct (missing column names are filled with an integer sequence). Unsupported by TensorSpec. """ if self.is_tensor_spec(): raise MlflowException("TensorSpec cannot be converted to spark dataframe") if len(self.inputs) == 1 and self.inputs[0].name is None: return self.inputs[0].type.to_spark() from pyspark.sql.types import StructField, StructType return StructType( [ StructField( name=col.name or str(i), dataType=col.type.to_spark(), nullable=not col.required ) for i, col in enumerate(self.inputs) ] )
[docs] def to_json(self) -> str: """Serialize into json string.""" return json.dumps([x.to_dict() for x in self.inputs])
[docs] def to_dict(self) -> List[Dict[str, Any]]: """Serialize into a jsonable dictionary.""" return [x.to_dict() for x in self.inputs]
[docs] @classmethod def from_json(cls, json_str: str): """Deserialize from a json string.""" def read_input(x: dict): return ( TensorSpec.from_json_dict(**x) if x["type"] == "tensor" else ColSpec.from_json_dict(**x) ) return cls([read_input(x) for x in json.loads(json_str)])
def __eq__(self, other) -> bool: if isinstance(other, Schema): return self.inputs == other.inputs else: return False def __repr__(self) -> str: return repr(self.inputs)
[docs]@experimental class ParamSpec: """ Specification used to represent parameters for the model. """ def __init__( self, name: str, dtype: Union[DataType, str], default: Union[DataType, List[DataType], None], shape: Optional[Tuple[int, ...]] = None, ): self._name = str(name) self._shape = tuple(shape) if shape is not None else None try: self._dtype = DataType[dtype] if isinstance(dtype, str) else dtype except KeyError: supported_types = [t.name for t in DataType if t.name != "binary"] raise MlflowException.invalid_parameter_value( f"Unsupported type '{dtype}', expected instance of DataType or " f"one of {supported_types}", ) if not isinstance(self.dtype, DataType): raise TypeError( "Expected mlflow.models.signature.Datatype or str for the 'dtype' " f"argument, but got {self.dtype.__class__}" ) if self.dtype == DataType.binary: raise MlflowException.invalid_parameter_value( f"Binary type is not supported for parameters, ParamSpec '{self.name}'" "has dtype 'binary'", ) # This line makes sure repr(self) works fine self._default = default self._default = self.validate_type_and_shape(repr(self), default, self.dtype, self.shape) @classmethod def validate_param_spec( cls, value: Union[DataType, List[DataType], None], param_spec: "ParamSpec" ): return cls.validate_type_and_shape( repr(param_spec), value, param_spec.dtype, param_spec.shape )
[docs] @classmethod def enforce_param_datatype(cls, name, value, dtype: DataType): """ Enforce the value matches the data type. The following type conversions are allowed: 1. int -> long, float, double 2. long -> float, double 3. float -> double 4. any -> datetime (try conversion) Any other type mismatch will raise error. Args: name: parameter name value: parameter value t: expected data type """ if value is None: return if dtype == DataType.datetime: try: datetime_value = np.datetime64(value).item() if isinstance(datetime_value, int): raise MlflowException.invalid_parameter_value( f"Invalid value for param {name}, it should " f"be convertible to datetime.date/datetime, got {value}" ) return datetime_value except ValueError as e: raise MlflowException.invalid_parameter_value( f"Failed to convert value {value} from type {type(value).__name__} " f"to {dtype} for param {name}" ) from e # Note that np.isscalar(datetime.date(...)) is False if not np.isscalar(value): raise MlflowException.invalid_parameter_value( f"Value should be a scalar for param {name}, got {value}" ) # Always convert to python native type for params if getattr(DataType, f"is_{dtype.name}")(value): return DataType[dtype.name].to_python()(value) if ( ( DataType.is_integer(value) and dtype in (DataType.long, DataType.float, DataType.double) ) or (DataType.is_long(value) and dtype in (DataType.float, DataType.double)) or (DataType.is_float(value) and dtype == DataType.double) ): try: return DataType[dtype.name].to_python()(value) except ValueError as e: raise MlflowException.invalid_parameter_value( f"Failed to convert value {value} from type {type(value).__name__} " f"to {dtype} for param {name}" ) from e raise MlflowException.invalid_parameter_value( f"Incompatible types for param {name}. Can not safely convert {type(value).__name__} " f"to {dtype}.", )
[docs] @classmethod def validate_type_and_shape( cls, spec: str, value: Union[DataType, List[DataType], None], value_type: DataType, shape: Optional[Tuple[int, ...]], ): """ Validate that the value has the expected type and shape. """ def _is_1d_array(value): return isinstance(value, (list, np.ndarray)) and np.array(value).ndim == 1 if shape is None: return cls.enforce_param_datatype(f"{spec} with shape None", value, value_type) elif shape == (-1,): if not _is_1d_array(value): raise MlflowException.invalid_parameter_value( f"Value must be a 1D array with shape (-1,) for param {spec}, " f"received {type(value).__name__} with ndim {np.array(value).ndim}", ) return [ cls.enforce_param_datatype(f"{spec} internal values", v, value_type) for v in value ] else: raise MlflowException.invalid_parameter_value( "Shape must be None for scalar value or (-1,) for 1D array value " f"for ParamSpec {spec}), received {shape}", )
@property def name(self) -> str: """The name of the parameter.""" return self._name @property def dtype(self) -> DataType: """The parameter data type.""" return self._dtype @property def default(self) -> Union[DataType, List[DataType], None]: """Default value of the parameter.""" return self._default @property def shape(self) -> Optional[tuple]: """ The parameter shape. If shape is None, the parameter is a scalar. """ return self._shape
[docs] class ParamSpecTypedDict(TypedDict): name: str type: str default: Union[DataType, List[DataType], None] shape: Optional[Tuple[int, ...]]
def to_dict(self) -> ParamSpecTypedDict: if self.shape is None: default_value = ( self.default.isoformat() if self.dtype.name == "datetime" else self.default ) elif self.shape == (-1,): default_value = ( [v.isoformat() for v in self.default] if self.dtype.name == "datetime" else self.default ) return { "name": self.name, "type": self.dtype.name, "default": default_value, "shape": self.shape, } def __eq__(self, other) -> bool: if isinstance(other, ParamSpec): return ( self.name == other.name and self.dtype == other.dtype and self.default == other.default and self.shape == other.shape ) return False def __repr__(self) -> str: shape = f" (shape: {self.shape})" if self.shape is not None else "" return f"{self.name!r}: {self.dtype!r} (default: {self.default}){shape}"
[docs] @classmethod def from_json_dict(cls, **kwargs): """ Deserialize from a json loaded dictionary. The dictionary is expected to contain `name`, `type` and `default` keys. """ # For backward compatibility, we accept both `type` and `dtype` keys required_keys1 = {"name", "dtype", "default"} required_keys2 = {"name", "type", "default"} if not (required_keys1.issubset(kwargs) or required_keys2.issubset(kwargs)): raise MlflowException.invalid_parameter_value( "Missing keys in ParamSpec JSON. Expected to find " "keys `name`, `type`(or `dtype`) and `default`. " f"Received keys: {kwargs.keys()}" ) dtype = kwargs.get("type") or kwargs.get("dtype") return cls( name=str(kwargs["name"]), dtype=DataType[dtype], default=kwargs["default"], shape=kwargs.get("shape"), )
[docs]@experimental class ParamSchema: """ Specification of parameters applicable to the model. ParamSchema is represented as a list of :py:class:`ParamSpec`. """ def __init__(self, params: List[ParamSpec]): if not all(isinstance(x, ParamSpec) for x in params): raise MlflowException.invalid_parameter_value( f"ParamSchema inputs only accept {ParamSchema.__class__}" ) if duplicates := self._find_duplicates(params): raise MlflowException.invalid_parameter_value( f"Duplicated parameters found in schema: {duplicates}" ) self._params = params @staticmethod def _find_duplicates(params: List[ParamSpec]) -> List[str]: param_names = [param_spec.name for param_spec in params] uniq_param = set() duplicates = [] for name in param_names: if name in uniq_param: duplicates.append(name) else: uniq_param.add(name) return duplicates def __len__(self): return len(self._params) def __iter__(self): return iter(self._params) @property def params(self) -> List[ParamSpec]: """Representation of ParamSchema as a list of ParamSpec.""" return self._params
[docs] def to_json(self) -> str: """Serialize into json string.""" return json.dumps(self.to_dict())
[docs] @classmethod def from_json(cls, json_str: str): """Deserialize from a json string.""" return cls([ParamSpec.from_json_dict(**x) for x in json.loads(json_str)])
[docs] def to_dict(self) -> List[Dict[str, Any]]: """Serialize into a jsonable dictionary.""" return [x.to_dict() for x in self.params]
def __eq__(self, other) -> bool: if isinstance(other, ParamSchema): return self.params == other.params return False def __repr__(self) -> str: return repr(self.params)
def _map_field_type(field): field_type_mapping = { bool: "boolean", int: "long", # int is mapped to long to support 64-bit integers builtins.float: "float", str: "string", bytes: "binary", dt.date: "datetime", } return field_type_mapping.get(field) @experimental def convert_dataclass_to_schema(dataclass): """ Converts a given dataclass into a Schema object. The dataclass must include type hints for all its fields. Fields can be of basic types, other dataclasses, or Lists/Optional of these types. Union types are not supported. Only the top-level fields are directly converted to ColSpecs, while nested fields are converted into nested Object types. """ inputs = [] for field_name, field_type in dataclass.__annotations__.items(): # Determine the type and handle Optional and List correctly is_optional = False effective_type = field_type if get_origin(field_type) == Union: if type(None) in get_args(field_type) and len(get_args(field_type)) == 2: # This is an Optional type; determine the effective type excluding None is_optional = True effective_type = next(t for t in get_args(field_type) if t is not type(None)) else: raise MlflowException( "Only Optional[...] is supported as a Union type in dataclass fields" ) if get_origin(effective_type) == list: # It's a list, check the type within the list list_type = get_args(effective_type)[0] if is_dataclass(list_type): dtype = _convert_dataclass_to_nested_object(list_type) # Convert to nested Object inputs.append( ColSpec(type=Array(dtype=dtype), name=field_name, required=not is_optional) ) else: if dtype := _map_field_type(list_type): inputs.append( ColSpec( type=Array(dtype=dtype), name=field_name, required=not is_optional, ) ) else: raise MlflowException( f"List field type {list_type} is not supported in dataclass" f" {dataclass.__name__}" ) elif is_dataclass(effective_type): # It's a nested dataclass dtype = _convert_dataclass_to_nested_object(effective_type) # Convert to nested Object inputs.append( ColSpec( type=dtype, name=field_name, required=not is_optional, ) ) # confirm the effective type is a basic type elif dtype := _map_field_type(effective_type): # It's a basic type inputs.append( ColSpec( type=dtype, name=field_name, required=not is_optional, ) ) else: raise MlflowException( f"Unsupported field type {effective_type} in dataclass {dataclass.__name__}" ) return Schema(inputs=inputs) def _convert_dataclass_to_nested_object(dataclass): """ Convert a nested dataclass to an Object type used within a ColSpec. """ properties = [] for field_name, field_type in dataclass.__annotations__.items(): properties.append(_convert_field_to_property(field_name, field_type)) return Object(properties=properties) def _convert_field_to_property(field_name, field_type): """ Helper function to convert a single field to a Property object suitable for inclusion in an Object. """ is_optional = False effective_type = field_type if get_origin(field_type) == Union and type(None) in get_args(field_type): is_optional = True effective_type = next(t for t in get_args(field_type) if t is not type(None)) if get_origin(effective_type) == list: list_type = get_args(effective_type)[0] return Property( name=field_name, dtype=Array(dtype=_map_field_type(list_type)), required=not is_optional, ) elif is_dataclass(effective_type): return Property( name=field_name, dtype=_convert_dataclass_to_nested_object(effective_type), required=not is_optional, ) else: return Property( name=field_name, dtype=_map_field_type(effective_type), required=not is_optional, )