如何在PySpark中使用UnaryTransformer？

from pyspark.ml import Transformer
from pyspark.ml.param.shared import HasInputCol, HasOutputCol, Param
from pyspark.ml.util import DefaultParamsReadable, DefaultParamsWritable
from pyspark.sql.functions import udf
from pyspark.sql.types import *

class MyUnaryTransformer(Transformer, DefaultParamsReadable, DefaultParamsWritable, HasInputCol, HasOutputCol):
    def __init__(self, inputCol=None, outputCol=None):
        super(MyUnaryTransformer, self).__init__()
        self._setDefault(inputCol=None, outputCol=None)
        kwargs = self._input_kwargs
        self.setParams(**kwargs)

    def setInputCol(self, value):
        return self._set(inputCol=value)

    def getInputCol(self):
        return self.getOrDefault(self.inputCol)

    def setOutputCol(self, value):
        return self._set(outputCol=value)

    def getOutputCol(self):
        return self.getOrDefault(self.outputCol)

    def transform(self, dataset):
        udf_func = udf(self._transform, self._get_output_data_type())
        return dataset.withColumn(self.getOutputCol(), udf_func(dataset[self.getInputCol()]))

    def _transform(self, value):
        # 自定义转换逻辑，将输入数据转换为输出数据
        # 这里可以使用任何你想要的转换操作，例如数据清洗、特征提取等
        return value

    def _get_output_data_type(self):
        # 返回输出数据的类型
        return StringType()

transformer = MyUnaryTransformer(inputCol="inputCol", outputCol="outputCol")

transformed_data = transformer.transform(input_data)