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

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from abc import ABC, abstractmethod

from pyflink.ml.core.wrapper import JavaModel, JavaEstimator, JavaTransformer

JAVA_FEATURE_PACKAGE_NAME = "org.apache.flink.ml.feature"


class JavaFeatureTransformer(JavaTransformer, ABC):
    """
    Wrapper class for a Java Feature Transformer.
    """

    def __init__(self, java_transformer):
        super(JavaFeatureTransformer, self).__init__(java_transformer)

    @classmethod
    def _java_stage_path(cls) -> str:
        return ".".join(
            [JAVA_FEATURE_PACKAGE_NAME,
             cls._java_transformer_package_name(),
             cls._java_transformer_class_name()])

    @classmethod
    @abstractmethod
    def _java_transformer_package_name(cls) -> str:
        pass

    @classmethod
    @abstractmethod
    def _java_transformer_class_name(cls) -> str:
        pass


class JavaFeatureModel(JavaModel, ABC):
    """
    Wrapper class for a Java Feature Model.
    """

    def __init__(self, java_model):
        super(JavaFeatureModel, self).__init__(java_model)

    @classmethod
    def _java_stage_path(cls) -> str:
        return ".".join(
            [JAVA_FEATURE_PACKAGE_NAME,
             cls._java_model_package_name(),
             cls._java_model_class_name()])

    @classmethod
    @abstractmethod
    def _java_model_package_name(cls) -> str:
        pass

    @classmethod
    @abstractmethod
    def _java_model_class_name(cls) -> str:
        pass


class JavaFeatureEstimator(JavaEstimator, ABC):
    """
    Wrapper class for a Java Feature Estimator.
    """

    def __init__(self):
        super(JavaFeatureEstimator, self).__init__()

    @classmethod
    def _java_stage_path(cls) -> str:
        return ".".join(
            [JAVA_FEATURE_PACKAGE_NAME,
             cls._java_estimator_package_name(),
             cls._java_estimator_class_name()])

    @classmethod
    @abstractmethod
    def _java_estimator_package_name(cls) -> str:
        pass

    @classmethod
    @abstractmethod
    def _java_estimator_class_name(cls) -> str:
        pass