flink-ml-python/pyflink/ml/lib/feature/elementwiseproduct.py

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from pyflink.ml.core.param import ParamValidators, Param, VectorParam
from pyflink.ml.core.wrapper import JavaWithParams
from pyflink.ml.lib.feature.common import JavaFeatureTransformer
from pyflink.ml.lib.param import HasInputCol, HasOutputCol
from pyflink.ml.core.linalg import Vector


class _ElementwiseProductParams(
    JavaWithParams,
    HasInputCol,
    HasOutputCol
):
    """
    Params for :class:`ElementwiseProduct`.
    """

    SCALING_VEC: Param[Vector] = VectorParam(
        "scaling_vec",
        "the scaling vector to multiply with input vectors using hadamard product.",
        None,
        ParamValidators.not_null())

    def __init__(self, java_params):
        super(_ElementwiseProductParams, self).__init__(java_params)

    def set_scaling_vec(self, value: Vector):
        return self.set(self.SCALING_VEC, value)

    def get_scaling_vec(self) -> Vector:
        return self.get(self.SCALING_VEC)

    @property
    def scaling_vec(self) -> Vector:
        return self.get_scaling_vec()


class ElementwiseProduct(JavaFeatureTransformer, _ElementwiseProductParams):
    """
    A Transformer that multiplies each input vector with a given scaling vector using Hadamard
    product.

    If the size of the input vector does not equal the size of the scaling vector,
    the transformer will throw IllegalArgumentException.
    """

    def __init__(self, java_model=None):
        super(ElementwiseProduct, self).__init__(java_model)

    @classmethod
    def _java_transformer_package_name(cls) -> str:
        return "elementwiseproduct"

    @classmethod
    def _java_transformer_class_name(cls) -> str:
        return "ElementwiseProduct"