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.Schema(cols: List[mlflow.types.schema.ColSpec])[source] Bases:
objectSpecification of types and column names in a dataset.
Schema is represented as a list of
ColSpec. The columns in a schema can be named, with unique non empty name for every column, or unnamed with implicit integer index defined by their list indices. Combination of named and unnamed columns is 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).
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column_names() → List[Union[str, int]][source] Get list of column names or range of indices if the schema has no column names.
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column_types() → List[mlflow.types.schema.DataType][source] Get column types of the columns in the dataset.
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classmethod
from_json(json_str: str)[source] Deserialize from a json string.
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has_column_names() → bool[source] Return true iff this schema declares column names, false otherwise.
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numpy_types() → List[numpy.dtype][source] Convenience shortcut to get the datatypes as numpy types.
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pandas_types() → List[numpy.dtype][source] Convenience shortcut to get the datatypes as pandas types.
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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.ColSpec(type: mlflow.types.schema.DataType, name: Optional[str] = None)[source] Bases:
objectSpecification of name and type of a single column in a dataset.