mlflow.types
The mlflow.types
module defines data types and utilities to be used by other mlflow
components to describe interface independent of other frameworks or languages.
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class
mlflow.types.
ColSpec
(type: Union[mlflow.types.schema.DataType, str], name: Optional[str] = None, optional: bool = False)[source] Bases:
object
Specification of name and type of a single column in a dataset.
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class
mlflow.types.
DataType
(value)[source] Bases:
enum.Enum
MLflow data types.
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to_numpy
() → numpy.dtype[source] Get equivalent numpy data type.
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to_pandas
() → numpy.dtype[source] Get equivalent pandas data type.
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class
mlflow.types.
Schema
(inputs: List[Union[mlflow.types.schema.ColSpec, mlflow.types.schema.TensorSpec]])[source] Bases:
object
Specification of a dataset.
Schema is represented as a list of
ColSpec
orTensorSpec
. 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
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.-
as_spark_schema
()[source] 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.
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classmethod
from_json
(json_str: str)[source] Deserialize from a json string.
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has_input_names
() → bool[source] Return true iff this schema declares names, false otherwise.
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input_names
() → List[Union[str, int]][source] Get list of data names or range of indices if the schema has no names.
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input_types
() → List[Union[mlflow.types.schema.DataType, numpy.dtype]][source] Get types for each column in the schema.
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input_types_dict
() → Dict[str, Union[mlflow.types.schema.DataType, numpy.dtype]][source] Maps column names to types, iff this schema declares names.
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is_tensor_spec
() → bool[source] Return true iff this schema is specified using TensorSpec
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numpy_types
() → List[numpy.dtype][source] Convenience shortcut to get the datatypes as numpy types.
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optional_input_names
() → List[Union[str, int]][source] Note
Experimental: This function may change or be removed in a future release without warning.
Get list of optional data names or range of indices if schema has no names.
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pandas_types
() → List[numpy.dtype][source] Convenience shortcut to get the datatypes as pandas types. Unsupported by TensorSpec.
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required_input_names
() → List[Union[str, int]][source] Get list of required data names or range of indices if schema has no names.
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to_dict
() → List[Dict[str, Any]][source] Serialize into a jsonable dictionary.
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to_json
() → str[source] Serialize into json string.
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class
mlflow.types.
TensorSpec
(type: numpy.dtype, shape: Union[tuple, list], name: Optional[str] = None)[source] Bases:
object
Specification used to represent a dataset stored as a Tensor.
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classmethod
from_json_dict
(**kwargs)[source] Deserialize from a json loaded dictionary. The dictionary is expected to contain type and tensor-spec keys.
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property
optional
Whether this tensor is optional.
Note
Experimental: This property may change or be removed in a future release without warning.
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property
type
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.
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classmethod