schema.py

import json
from enum import Enum

import numpy as np
import pandas as pd
import string
from typing import Dict, Any, List, Union, Optional

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


def _pandas_string_type():
    try:
        return pd.StringDtype()
    except AttributeError:
        return np.object


class DataType(Enum):
    """
    MLflow data types.
    """

    def __new__(cls, value, numpy_type, spark_type, pandas_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
        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")
    """Logical data (True, False) ."""
    integer = (2, np.dtype("int32"), "IntegerType")
    """32b signed integer numbers."""
    long = (3, np.dtype("int64"), "LongType")
    """64b signed integer numbers. """
    float = (4, np.dtype("float32"), "FloatType")
    """32b floating point numbers. """
    double = (5, np.dtype("float64"), "DoubleType")
    """64b floating point numbers. """
    string = (6, np.dtype("str"), "StringType", _pandas_string_type())
    """Text data."""
    binary = (7, np.dtype("bytes"), "BinaryType", np.object)
    """Sequence of raw bytes."""
    datetime = (8, np.dtype("datetime64"), "TimestampType")
    """64b datetime data."""

    def __repr__(self):
        return self.name

    def to_numpy(self) -> np.dtype:
        """Get equivalent numpy data type."""
        return self._numpy_type

    def to_pandas(self) -> np.dtype:
        """Get equivalent pandas data type."""
        return self._pandas_type

    def to_spark(self):
        import pyspark.sql.types

        return getattr(pyspark.sql.types, self._spark_type)()


class ColSpec:
    """
    Specification of name and type of a single column in a dataset.
    """

    def __init__(
        self, type: DataType, name: Optional[str] = None  # pylint: disable=redefined-builtin
    ):
        self._name = name
        try:
            self._type = DataType[type] if isinstance(type, str) else type
        except KeyError:
            raise MlflowException(
                "Unsupported type '{0}', expected instance of DataType or "
                "one of {1}".format(type, [t.name for t in DataType])
            )
        if not isinstance(self.type, DataType):
            raise TypeError(
                "Expected mlflow.models.signature.Datatype or str for the 'type' "
                "argument, but got {}".format(self.type.__class__)
            )

    @property
    def type(self) -> DataType:
        """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

    def to_dict(self) -> Dict[str, Any]:
        if self.name is None:
            return {"type": self.type.name}
        else:
            return {"name": self.name, "type": self.type.name}

    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
        return False

    def __repr__(self) -> str:
        if self.name is None:
            return repr(self.type)
        else:
            return "{name}: {type}".format(name=repr(self.name), type=repr(self.type))


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(
                "Expected `type` to be instance of `{0}`, received `{1}`".format(
                    np.dtype, type.__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"
                ' np.dtype("{0}") instead'.format(dtype.name.rstrip(string.digits))
            )

        if not isinstance(shape, (tuple, list)):
            raise TypeError(
                "Expected `shape` to be instance of `{0}` or `{1}`, received `{2}`".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 "Tensor({type}, {shape})".format(type=repr(self.dtype.name), shape=repr(self.shape))


class TensorSpec:
    """
    Specification used to represent a dataset stored as a Tensor.
    """

    def __init__(
        self,
        type: np.dtype,  # pylint: disable=redefined-builtin
        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

    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()}

    @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 "{name}: {info}".format(name=repr(self.name), info=repr(self._tensorInfo))


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 (
            all(map(lambda x: x.name is None, inputs))
            or all(map(lambda x: x.name is not None, inputs))
        ):
            raise MlflowException(
                "Creating Schema with a combination of named and unnamed inputs "
                "is not allowed. Got input names {}".format([x.name for x in inputs])
            )
        if not (
            all(map(lambda x: isinstance(x, TensorSpec), inputs))
            or all(map(lambda x: isinstance(x, ColSpec), 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(map(lambda x: isinstance(x, TensorSpec), inputs))
            and len(inputs) > 1
            and any(map(lambda x: x.name is None, inputs))
        ):
            raise MlflowException(
                "Creating Schema with multiple unnamed TensorSpecs is not supported. "
                "Please provide names for each TensorSpec."
            )
        self._inputs = inputs

    @property
    def inputs(self) -> List[Union[ColSpec, TensorSpec]]:
        """Representation of a dataset that defines this schema."""
        return self._inputs

    @property
    @deprecated(alternative="mlflow.types.Schema.inputs", since="1.14")
    def columns(self) -> List[ColSpec]:
        """
        .. deprecated:: 1.14
          Please use :func:`mlflow.types.Schema.inputs`
          The list of columns that defines this schema.

        """
        if self.is_tensor_spec():
            raise MlflowException("Not supported by TensorSpec, use `inputs` instead")
        return self._inputs

    def is_tensor_spec(self) -> bool:
        """Return true iff this schema is specified using TensorSpec"""
        return self.inputs and isinstance(self.inputs[0], TensorSpec)

    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)]

    @deprecated(alternative="mlflow.types.Schema.input_names", since="1.14")
    def column_names(self) -> List[Union[str, int]]:
        """
        .. deprecated:: 1.14
          Please use :func:`mlflow.types.Schema.input_names()`
          Get list of column names or range of indices if the schema has no column names.

        """
        if self.is_tensor_spec():
            raise MlflowException("Not supported by TensorSpec, use input_names() instead")
        return [x.name or i for i, x in enumerate(self.columns)]

    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

    @deprecated(alternative="mlflow.types.Schema.has_input_names", since="1.14")
    def has_column_names(self) -> bool:
        """
        .. deprecated:: 1.14
          Please use :func:`mlflow.types.Schema.has_input_names()`
          Return true iff this schema declares column names, false otherwise.

        """
        if self.is_tensor_spec():
            raise MlflowException("Not supported by TensorSpec, use has_input_names() instead")
        return self.columns and self.columns[0].name is not None

    def input_types(self) -> List[Union[DataType, np.dtype]]:
        """Get types of the represented dataset."""
        return [x.type for x in self.inputs]

    @deprecated(alternative="mlflow.types.Schema.input_types", since="1.14")
    def column_types(self) -> List[DataType]:
        """
        .. deprecated:: 1.14
          Please use :func:`mlflow.types.Schema.input_types()`
          Get types of the represented dataset. Unsupported by TensorSpec.

        """
        if self.is_tensor_spec():
            raise MlflowException("TensorSpec only supports numpy types, use numpy_types() instead")
        return [x.type for x in self.columns]

    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]
        return [x.type.to_numpy() for x in self.inputs]

    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")
        return [x.type.to_pandas() for x in self.inputs]

    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 StructType, StructField

        return StructType(
            [
                StructField(name=col.name or str(i), dataType=col.type.to_spark())
                for i, col in enumerate(self.inputs)
            ]
        )

    def to_json(self) -> str:
        """Serialize into json string."""
        return json.dumps([x.to_dict() for x in self.inputs])

    def to_dict(self) -> List[Dict[str, Any]]:
        """Serialize into a jsonable dictionary."""
        return [x.to_dict() for x in self.inputs]

    @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(**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)