Source code for pyspark.ml.base

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# The ASF licenses this file to You under the Apache License, Version 2.0
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from abc import ABCMeta, abstractmethod
import copy
import threading
from typing import (
    Any,
    Callable,
    Generic,
    Iterator,
    List,
    Optional,
    Sequence,
    Tuple,
    TypeVar,
    Union,
    cast,
    overload,
    TYPE_CHECKING,
)

from pyspark import since
from pyspark.ml.param import P
from pyspark.ml.common import inherit_doc
from pyspark.ml.param.shared import (
    HasInputCol,
    HasOutputCol,
    HasLabelCol,
    HasFeaturesCol,
    HasPredictionCol,
    Params,
)
from pyspark.sql.dataframe import DataFrame
from pyspark.sql.functions import udf
from pyspark.sql.types import DataType, StructField, StructType

if TYPE_CHECKING:
    from pyspark.ml._typing import ParamMap

T = TypeVar("T")
M = TypeVar("M", bound="Transformer")


class _FitMultipleIterator(Generic[M]):
    """
    Used by default implementation of Estimator.fitMultiple to produce models in a thread safe
    iterator. This class handles the simple case of fitMultiple where each param map should be
    fit independently.

    Parameters
    ----------
    fitSingleModel : function
        Callable[[int], Transformer] which fits an estimator to a dataset.
        `fitSingleModel` may be called up to `numModels` times, with a unique index each time.
        Each call to `fitSingleModel` with an index should return the Model associated with
        that index.
    numModel : int
        Number of models this iterator should produce.

    Notes
    -----
    See :py:meth:`Estimator.fitMultiple` for more info.
    """

    def __init__(self, fitSingleModel: Callable[[int], M], numModels: int):
        """ """
        self.fitSingleModel = fitSingleModel
        self.numModel = numModels
        self.counter = 0
        self.lock = threading.Lock()

    def __iter__(self) -> Iterator[Tuple[int, M]]:
        return self

    def __next__(self) -> Tuple[int, M]:
        with self.lock:
            index = self.counter
            if index >= self.numModel:
                raise StopIteration("No models remaining.")
            self.counter += 1
        return index, self.fitSingleModel(index)

    def next(self) -> Tuple[int, M]:
        """For python2 compatibility."""
        return self.__next__()


@inherit_doc
class Estimator(Params, Generic[M], metaclass=ABCMeta):
    """
    Abstract class for estimators that fit models to data.

    .. versionadded:: 1.3.0
    """

    @abstractmethod
    def _fit(self, dataset: DataFrame) -> M:
        """
        Fits a model to the input dataset. This is called by the default implementation of fit.


        Parameters
        ----------
        dataset : :py:class:`pyspark.sql.DataFrame`
            input dataset

        Returns
        -------
        :class:`Transformer`
            fitted model
        """
        raise NotImplementedError()

    def fitMultiple(
        self, dataset: DataFrame, paramMaps: Sequence["ParamMap"]
    ) -> Iterator[Tuple[int, M]]:
        """
        Fits a model to the input dataset for each param map in `paramMaps`.

        .. versionadded:: 2.3.0

        Parameters
        ----------
        dataset : :py:class:`pyspark.sql.DataFrame`
            input dataset.
        paramMaps : :py:class:`collections.abc.Sequence`
            A Sequence of param maps.

        Returns
        -------
        :py:class:`_FitMultipleIterator`
            A thread safe iterable which contains one model for each param map. Each
            call to `next(modelIterator)` will return `(index, model)` where model was fit
            using `paramMaps[index]`. `index` values may not be sequential.
        """
        estimator = self.copy()

        def fitSingleModel(index: int) -> M:
            return estimator.fit(dataset, paramMaps[index])

        return _FitMultipleIterator(fitSingleModel, len(paramMaps))

    @overload
    def fit(self, dataset: DataFrame, params: Optional["ParamMap"] = ...) -> M:
        ...

    @overload
    def fit(
        self, dataset: DataFrame, params: Union[List["ParamMap"], Tuple["ParamMap"]]
    ) -> List[M]:
        ...

    def fit(
        self,
        dataset: DataFrame,
        params: Optional[Union["ParamMap", List["ParamMap"], Tuple["ParamMap"]]] = None,
    ) -> Union[M, List[M]]:
        """
        Fits a model to the input dataset with optional parameters.

        .. versionadded:: 1.3.0

        Parameters
        ----------
        dataset : :py:class:`pyspark.sql.DataFrame`
            input dataset.
        params : dict or list or tuple, optional
            an optional param map that overrides embedded params. If a list/tuple of
            param maps is given, this calls fit on each param map and returns a list of
            models.

        Returns
        -------
        :py:class:`Transformer` or a list of :py:class:`Transformer`
            fitted model(s)
        """
        if params is None:
            params = dict()
        if isinstance(params, (list, tuple)):
            models: List[Optional[M]] = [None] * len(params)
            for index, model in self.fitMultiple(dataset, params):
                models[index] = model
            return cast(List[M], models)
        elif isinstance(params, dict):
            if params:
                return self.copy(params)._fit(dataset)
            else:
                return self._fit(dataset)
        else:
            raise TypeError(
                "Params must be either a param map or a list/tuple of param maps, "
                "but got %s." % type(params)
            )


@inherit_doc
class Transformer(Params, metaclass=ABCMeta):
    """
    Abstract class for transformers that transform one dataset into another.

    .. versionadded:: 1.3.0
    """

    @abstractmethod
    def _transform(self, dataset: DataFrame) -> DataFrame:
        """
        Transforms the input dataset.

        Parameters
        ----------
        dataset : :py:class:`pyspark.sql.DataFrame`
            input dataset.

        Returns
        -------
        :py:class:`pyspark.sql.DataFrame`
            transformed dataset
        """
        raise NotImplementedError()

    def transform(self, dataset: DataFrame, params: Optional["ParamMap"] = None) -> DataFrame:
        """
        Transforms the input dataset with optional parameters.

        .. versionadded:: 1.3.0

        Parameters
        ----------
        dataset : :py:class:`pyspark.sql.DataFrame`
            input dataset
        params : dict, optional
            an optional param map that overrides embedded params.

        Returns
        -------
        :py:class:`pyspark.sql.DataFrame`
            transformed dataset
        """
        if params is None:
            params = dict()
        if isinstance(params, dict):
            if params:
                return self.copy(params)._transform(dataset)
            else:
                return self._transform(dataset)
        else:
            raise TypeError("Params must be a param map but got %s." % type(params))


@inherit_doc
class Model(Transformer, metaclass=ABCMeta):
    """
    Abstract class for models that are fitted by estimators.

    .. versionadded:: 1.4.0
    """

    pass


@inherit_doc
class UnaryTransformer(HasInputCol, HasOutputCol, Transformer):
    """
    Abstract class for transformers that take one input column, apply transformation,
    and output the result as a new column.

    .. versionadded:: 2.3.0
    """

    def setInputCol(self: P, value: str) -> P:
        """
        Sets the value of :py:attr:`inputCol`.
        """
        return self._set(inputCol=value)

    def setOutputCol(self: P, value: str) -> P:
        """
        Sets the value of :py:attr:`outputCol`.
        """
        return self._set(outputCol=value)

    @abstractmethod
    def createTransformFunc(self) -> Callable[..., Any]:
        """
        Creates the transform function using the given param map. The input param map already takes
        account of the embedded param map. So the param values should be determined
        solely by the input param map.
        """
        raise NotImplementedError()

    @abstractmethod
    def outputDataType(self) -> DataType:
        """
        Returns the data type of the output column.
        """
        raise NotImplementedError()

    @abstractmethod
    def validateInputType(self, inputType: DataType) -> None:
        """
        Validates the input type. Throw an exception if it is invalid.
        """
        raise NotImplementedError()

    def transformSchema(self, schema: StructType) -> StructType:
        inputType = schema[self.getInputCol()].dataType
        self.validateInputType(inputType)
        if self.getOutputCol() in schema.names:
            raise ValueError("Output column %s already exists." % self.getOutputCol())
        outputFields = copy.copy(schema.fields)
        outputFields.append(StructField(self.getOutputCol(), self.outputDataType(), nullable=False))
        return StructType(outputFields)

    def _transform(self, dataset: DataFrame) -> DataFrame:
        self.transformSchema(dataset.schema)
        transformUDF = udf(self.createTransformFunc(), self.outputDataType())
        transformedDataset = dataset.withColumn(
            self.getOutputCol(), transformUDF(dataset[self.getInputCol()])
        )
        return transformedDataset


@inherit_doc
class _PredictorParams(HasLabelCol, HasFeaturesCol, HasPredictionCol):
    """
    Params for :py:class:`Predictor` and :py:class:`PredictorModel`.

    .. versionadded:: 3.0.0
    """

    pass


@inherit_doc
class Predictor(Estimator[M], _PredictorParams, metaclass=ABCMeta):
    """
    Estimator for prediction tasks (regression and classification).
    """

    @since("3.0.0")
    def setLabelCol(self: P, value: str) -> P:
        """
        Sets the value of :py:attr:`labelCol`.
        """
        return self._set(labelCol=value)

    @since("3.0.0")
    def setFeaturesCol(self: P, value: str) -> P:
        """
        Sets the value of :py:attr:`featuresCol`.
        """
        return self._set(featuresCol=value)

    @since("3.0.0")
    def setPredictionCol(self: P, value: str) -> P:
        """
        Sets the value of :py:attr:`predictionCol`.
        """
        return self._set(predictionCol=value)


@inherit_doc
class PredictionModel(Model, _PredictorParams, Generic[T], metaclass=ABCMeta):
    """
    Model for prediction tasks (regression and classification).
    """

    @since("3.0.0")
    def setFeaturesCol(self: P, value: str) -> P:
        """
        Sets the value of :py:attr:`featuresCol`.
        """
        return self._set(featuresCol=value)

    @since("3.0.0")
    def setPredictionCol(self: P, value: str) -> P:
        """
        Sets the value of :py:attr:`predictionCol`.
        """
        return self._set(predictionCol=value)

    @property
    @abstractmethod
    @since("2.1.0")
    def numFeatures(self) -> int:
        """
        Returns the number of features the model was trained on. If unknown, returns -1
        """
        raise NotImplementedError()

    @abstractmethod
    @since("3.0.0")
    def predict(self, value: T) -> float:
        """
        Predict label for the given features.
        """
        raise NotImplementedError()