Source code for pyspark.ml.wrapper

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from abc import ABCMeta, abstractmethod
from typing import Any, Generic, Optional, List, Type, TypeVar, TYPE_CHECKING

from pyspark import since
from pyspark.sql import DataFrame
from pyspark.ml import Estimator, Predictor, PredictionModel, Transformer, Model
from pyspark.ml.base import _PredictorParams
from pyspark.ml.param import Param, Params
from pyspark.ml.util import _jvm
from pyspark.ml.common import inherit_doc, _java2py, _py2java


if TYPE_CHECKING:
    from pyspark.ml._typing import ParamMap
    from py4j.java_gateway import JavaObject, JavaClass


T = TypeVar("T")
JW = TypeVar("JW", bound="JavaWrapper")
JM = TypeVar("JM", bound="JavaTransformer")
JP = TypeVar("JP", bound="JavaParams")


class JavaWrapper:
    """
    Wrapper class for a Java companion object
    """

    def __init__(self, java_obj: Optional["JavaObject"] = None):
        super(JavaWrapper, self).__init__()
        self._java_obj = java_obj

    def __del__(self) -> None:
        from pyspark.core.context import SparkContext

        if SparkContext._active_spark_context and self._java_obj is not None:
            SparkContext._active_spark_context._gateway.detach(  # type: ignore[union-attr]
                self._java_obj
            )

    @classmethod
    def _create_from_java_class(cls: Type[JW], java_class: str, *args: Any) -> JW:
        """
        Construct this object from given Java classname and arguments
        """
        java_obj = JavaWrapper._new_java_obj(java_class, *args)
        return cls(java_obj)

    def _call_java(self, name: str, *args: Any) -> Any:
        from pyspark.core.context import SparkContext

        m = getattr(self._java_obj, name)
        sc = SparkContext._active_spark_context
        assert sc is not None

        java_args = [_py2java(sc, arg) for arg in args]
        return _java2py(sc, m(*java_args))

    @staticmethod
    def _new_java_obj(java_class: str, *args: Any) -> "JavaObject":
        """
        Returns a new Java object.
        """
        from pyspark.core.context import SparkContext

        sc = SparkContext._active_spark_context
        assert sc is not None

        java_obj = _jvm()
        for name in java_class.split("."):
            java_obj = getattr(java_obj, name)
        java_args = [_py2java(sc, arg) for arg in args]
        return java_obj(*java_args)

    @staticmethod
    def _new_java_array(pylist: List[Any], java_class: "JavaClass") -> "JavaObject":
        """
        Create a Java array of given java_class type. Useful for
        calling a method with a Scala Array from Python with Py4J.
        If the param pylist is a 2D array, then a 2D java array will be returned.
        The returned 2D java array is a square, non-jagged 2D array that is big
        enough for all elements. The empty slots in the inner Java arrays will
        be filled with null to make the non-jagged 2D array.

        Parameters
        ----------
        pylist : list
            Python list to convert to a Java Array.
        java_class : :py:class:`py4j.java_gateway.JavaClass`
            Java class to specify the type of Array. Should be in the
            form of sc._gateway.jvm.* (sc is a valid Spark Context).

            Example primitive Java classes:

            - basestring -> sc._gateway.jvm.java.lang.String
            - int -> sc._gateway.jvm.java.lang.Integer
            - float -> sc._gateway.jvm.java.lang.Double
            - bool -> sc._gateway.jvm.java.lang.Boolean

        Returns
        -------
        :py:class:`py4j.java_collections.JavaArray`
          Java Array of converted pylist.
        """
        from pyspark.core.context import SparkContext

        sc = SparkContext._active_spark_context
        assert sc is not None
        assert sc._gateway is not None

        java_array = None
        if len(pylist) > 0 and isinstance(pylist[0], list):
            # If pylist is a 2D array, then a 2D java array will be created.
            # The 2D array is a square, non-jagged 2D array that is big enough for all elements.
            inner_array_length = 0
            for i in range(len(pylist)):
                inner_array_length = max(inner_array_length, len(pylist[i]))
            java_array = sc._gateway.new_array(java_class, len(pylist), inner_array_length)
            for i in range(len(pylist)):
                for j in range(len(pylist[i])):
                    java_array[i][j] = pylist[i][j]
        else:
            java_array = sc._gateway.new_array(java_class, len(pylist))
            for i in range(len(pylist)):
                java_array[i] = pylist[i]
        return java_array


@inherit_doc
class JavaParams(JavaWrapper, Params, metaclass=ABCMeta):
    """
    Utility class to help create wrapper classes from Java/Scala
    implementations of pipeline components.
    """

    #: The param values in the Java object should be
    #: synced with the Python wrapper in fit/transform/evaluate/copy.

    def _make_java_param_pair(self, param: Param[T], value: T) -> "JavaObject":
        """
        Makes a Java param pair.
        """
        from pyspark.core.context import SparkContext

        sc = SparkContext._active_spark_context
        assert sc is not None and self._java_obj is not None

        param = self._resolveParam(param)
        java_param = self._java_obj.getParam(param.name)
        java_value = _py2java(sc, value)
        return java_param.w(java_value)

    def _transfer_params_to_java(self) -> None:
        """
        Transforms the embedded params to the companion Java object.
        """
        from pyspark.core.context import SparkContext

        assert self._java_obj is not None

        pair_defaults = []
        for param in self.params:
            if self.isSet(param):
                pair = self._make_java_param_pair(param, self._paramMap[param])
                self._java_obj.set(pair)
            if self.hasDefault(param):
                pair = self._make_java_param_pair(param, self._defaultParamMap[param])
                pair_defaults.append(pair)
        if len(pair_defaults) > 0:
            sc = SparkContext._active_spark_context
            assert sc is not None and sc._jvm is not None

            pair_defaults_seq = sc._jvm.PythonUtils.toSeq(pair_defaults)
            self._java_obj.setDefault(pair_defaults_seq)

    def _transfer_param_map_to_java(self, pyParamMap: "ParamMap") -> "JavaObject":
        """
        Transforms a Python ParamMap into a Java ParamMap.
        """
        paramMap = JavaWrapper._new_java_obj("org.apache.spark.ml.param.ParamMap")
        for param in self.params:
            if param in pyParamMap:
                pair = self._make_java_param_pair(param, pyParamMap[param])
                paramMap.put([pair])
        return paramMap

    def _create_params_from_java(self) -> None:
        """
        SPARK-10931: Temporary fix to create params that are defined in the Java obj but not here
        """
        assert self._java_obj is not None

        java_params = list(self._java_obj.params())
        from pyspark.ml.param import Param

        for java_param in java_params:
            java_param_name = java_param.name()
            if not hasattr(self, java_param_name):
                param: Param[Any] = Param(self, java_param_name, java_param.doc())
                setattr(param, "created_from_java_param", True)
                setattr(self, java_param_name, param)
                self._params = None  # need to reset so self.params will discover new params

    def _transfer_params_from_java(self) -> None:
        """
        Transforms the embedded params from the companion Java object.
        """
        from pyspark.core.context import SparkContext

        sc = SparkContext._active_spark_context
        assert sc is not None and self._java_obj is not None

        for param in self.params:
            if self._java_obj.hasParam(param.name):
                java_param = self._java_obj.getParam(param.name)
                # SPARK-14931: Only check set params back to avoid default params mismatch.
                if self._java_obj.isSet(java_param):
                    java_value = self._java_obj.getOrDefault(java_param)
                    if param.typeConverter.__name__.startswith("toList"):
                        value = [_java2py(sc, x) for x in list(java_value)]
                    else:
                        value = _java2py(sc, java_value)
                    self._set(**{param.name: value})
                # SPARK-10931: Temporary fix for params that have a default in Java
                if self._java_obj.hasDefault(java_param) and not self.isDefined(param):
                    value = _java2py(sc, self._java_obj.getDefault(java_param)).get()
                    self._setDefault(**{param.name: value})

    def _transfer_param_map_from_java(self, javaParamMap: "JavaObject") -> "ParamMap":
        """
        Transforms a Java ParamMap into a Python ParamMap.
        """
        from pyspark.core.context import SparkContext

        sc = SparkContext._active_spark_context
        assert sc is not None

        paramMap = dict()
        for pair in javaParamMap.toList():
            param = pair.param()
            if self.hasParam(str(param.name())):
                paramMap[self.getParam(param.name())] = _java2py(sc, pair.value())
        return paramMap

    @staticmethod
    def _empty_java_param_map() -> "JavaObject":
        """
        Returns an empty Java ParamMap reference.
        """
        return _jvm().org.apache.spark.ml.param.ParamMap()

    def _to_java(self) -> "JavaObject":
        """
        Transfer this instance's Params to the wrapped Java object, and return the Java object.
        Used for ML persistence.

        Meta-algorithms such as Pipeline should override this method.

        Returns
        -------
        py4j.java_gateway.JavaObject
            Java object equivalent to this instance.
        """
        self._transfer_params_to_java()
        return self._java_obj

    @staticmethod
    def _from_java(java_stage: "JavaObject") -> "JP":  # type: ignore
        """
        Given a Java object, create and return a Python wrapper of it.
        Used for ML persistence.

        Meta-algorithms such as Pipeline should override this method as a classmethod.
        """

        def __get_class(clazz: str) -> Type[JP]:
            """
            Loads Python class from its name.
            """
            parts = clazz.split(".")
            module = ".".join(parts[:-1])
            m = __import__(module, fromlist=[parts[-1]])
            return getattr(m, parts[-1])

        stage_name = java_stage.getClass().getName().replace("org.apache.spark", "pyspark")
        # Generate a default new instance from the stage_name class.
        py_type = __get_class(stage_name)
        if issubclass(py_type, JavaParams):
            # Load information from java_stage to the instance.
            py_stage = py_type()
            py_stage._java_obj = java_stage

            # SPARK-10931: Temporary fix so that persisted models would own params from Estimator
            if issubclass(py_type, JavaModel):
                py_stage._create_params_from_java()

            py_stage._resetUid(java_stage.uid())
            py_stage._transfer_params_from_java()
        elif hasattr(py_type, "_from_java"):
            py_stage = py_type._from_java(java_stage)
        else:
            raise NotImplementedError(
                "This Java stage cannot be loaded into Python currently: %r" % stage_name
            )
        return py_stage

    def copy(self: "JP", extra: Optional["ParamMap"] = None) -> "JP":
        """
        Creates a copy of this instance with the same uid and some
        extra params. This implementation first calls Params.copy and
        then make a copy of the companion Java pipeline component with
        extra params. So both the Python wrapper and the Java pipeline
        component get copied.

        Parameters
        ----------
        extra : dict, optional
            Extra parameters to copy to the new instance

        Returns
        -------
        :py:class:`JavaParams`
            Copy of this instance
        """
        if extra is None:
            extra = dict()
        that = super(JavaParams, self).copy(extra)
        if self._java_obj is not None:
            that._java_obj = self._java_obj.copy(self._empty_java_param_map())
            that._transfer_params_to_java()
        return that

    def clear(self, param: Param) -> None:
        """
        Clears a param from the param map if it has been explicitly set.
        """
        assert self._java_obj is not None

        super(JavaParams, self).clear(param)
        java_param = self._java_obj.getParam(param.name)
        self._java_obj.clear(java_param)


@inherit_doc
class JavaEstimator(JavaParams, Estimator[JM], metaclass=ABCMeta):
    """
    Base class for :py:class:`Estimator`s that wrap Java/Scala
    implementations.
    """

    @abstractmethod
    def _create_model(self, java_model: "JavaObject") -> JM:
        """
        Creates a model from the input Java model reference.
        """
        raise NotImplementedError()

    def _fit_java(self, dataset: DataFrame) -> "JavaObject":
        """
        Fits a Java model to the input dataset.

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

        Returns
        -------
        py4j.java_gateway.JavaObject
            fitted Java model
        """
        assert self._java_obj is not None

        self._transfer_params_to_java()
        return self._java_obj.fit(dataset._jdf)

    def _fit(self, dataset: DataFrame) -> JM:
        java_model = self._fit_java(dataset)
        model = self._create_model(java_model)
        return self._copyValues(model)


@inherit_doc
class JavaTransformer(JavaParams, Transformer, metaclass=ABCMeta):
    """
    Base class for :py:class:`Transformer`s that wrap Java/Scala
    implementations. Subclasses should ensure they have the transformer Java object
    available as _java_obj.
    """

    def _transform(self, dataset: DataFrame) -> DataFrame:
        assert self._java_obj is not None

        self._transfer_params_to_java()
        return DataFrame(self._java_obj.transform(dataset._jdf), dataset.sparkSession)


@inherit_doc
class JavaModel(JavaTransformer, Model, metaclass=ABCMeta):
    """
    Base class for :py:class:`Model`s that wrap Java/Scala
    implementations. Subclasses should inherit this class before
    param mix-ins, because this sets the UID from the Java model.
    """

    def __init__(self, java_model: Optional["JavaObject"] = None):
        """
        Initialize this instance with a Java model object.
        Subclasses should call this constructor, initialize params,
        and then call _transfer_params_from_java.

        This instance can be instantiated without specifying java_model,
        it will be assigned after that, but this scenario only used by
        :py:class:`JavaMLReader` to load models.  This is a bit of a
        hack, but it is easiest since a proper fix would require
        MLReader (in pyspark.ml.util) to depend on these wrappers, but
        these wrappers depend on pyspark.ml.util (both directly and via
        other ML classes).
        """
        super(JavaModel, self).__init__(java_model)
        if java_model is not None:
            # SPARK-10931: This is a temporary fix to allow models to own params
            # from estimators. Eventually, these params should be in models through
            # using common base classes between estimators and models.
            self._create_params_from_java()

            self._resetUid(java_model.uid())

    def __repr__(self) -> str:
        return self._call_java("toString")


@inherit_doc
class JavaPredictor(Predictor, JavaEstimator[JM], _PredictorParams, Generic[JM], metaclass=ABCMeta):
    """
    (Private) Java Estimator for prediction tasks (regression and classification).
    """

    pass


@inherit_doc
class JavaPredictionModel(PredictionModel[T], JavaModel, _PredictorParams):
    """
    (Private) Java Model for prediction tasks (regression and classification).
    """

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

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