Source code for mlflow.types.schema

import json
from enum import Enum

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

from mlflow.exceptions import MlflowException

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

[docs]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.""" def __repr__(self): return def __str(self): return
[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): import pyspark.sql.types return getattr(pyspark.sql.types, self._spark_type)()
[docs]class ColSpec(object): """ Specification of name and type of a single column in a dataset. """ def __init__(self, type: DataType, # pylint: disable=redefined-builtin name: Optional[str] = None): 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, [ 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 is None: return {"type":} else: return {"name":, "type":} def __eq__(self, other) -> bool: names_eq = ( is None and is None) or == return names_eq and self.type == other.type def __repr__(self) -> str: if is None: return repr(self.type) else: return "{name}: {type}".format(name=repr(, type=repr(self.type))
[docs]class Schema(object): """ Specification of types and column names in a dataset. Schema is represented as a list of :py:class:`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. """ def __init__(self, cols: List[ColSpec]): if not (all(map(lambda x: is None, cols)) or all(map(lambda x: is not None, cols))): raise MlflowException("Creating Schema with a combination of named and unnamed columns " "is not allowed. Got column names {}".format( [ for x in cols])) self._cols = cols @property def columns(self) -> List[ColSpec]: """The list of columns that defines this schema.""" return self._cols
[docs] def column_names(self) -> List[Union[str, int]]: """Get list of column names or range of indices if the schema has no column names.""" return [ or i for i, x in enumerate(self.columns)]
[docs] def has_column_names(self) -> bool: """ Return true iff this schema declares column names, false otherwise. """ return self.columns and self.columns[0].name is not None
[docs] def column_types(self) -> List[DataType]: """ Get column types of the columns in the dataset.""" return [x.type for x in self._cols]
[docs] def numpy_types(self) -> List[np.dtype]: """ Convenience shortcut to get the datatypes as numpy types.""" return [x.type.to_numpy() for x in self.columns]
[docs] def pandas_types(self) -> List[np.dtype]: """ Convenience shortcut to get the datatypes as pandas types.""" return [x.type.to_pandas() for x in self.columns]
[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). """ if len(self.columns) == 1 and self.columns[0].name is None: return self.columns[0].type.to_spark() from pyspark.sql.types import StructType, StructField return StructType([StructField( or str(i), dataType=col.type.to_spark()) for i, col in enumerate(self.columns)])
[docs] def to_json(self) -> str: """Serialize into json string.""" return json.dumps([x.to_dict() for x in self.columns])
[docs] def to_dict(self) -> List[Dict[str, Any]]: """Serialize into a jsonable dictionary.""" return [x.to_dict() for x in self.columns]
[docs] @classmethod def from_json(cls, json_str: str): """ Deserialize from a json string.""" return cls([ColSpec(**x) for x in json.loads(json_str)])
def __eq__(self, other) -> bool: if isinstance(other, Schema): return self.columns == other.columns else: return False def __repr__(self) -> str: return repr(self.columns)