如何在PySpark ML中创建自定义标记器

from pyspark.ml import Transformer
from pyspark.ml.param.shared import HasInputCol, HasOutputCol
from pyspark.ml.util import DefaultParamsReadable, DefaultParamsWritable
from pyspark.sql import functions as F

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

    def transform(self, dataset):
        tokenize_udf = F.udf(self._tokenize, ArrayType(StringType()))
        output_col = self.getOutputCol()
        return dataset.withColumn(output_col, tokenize_udf(dataset[self.getInputCol()]))

    def _tokenize(self, text):
        # 在这里实现分词逻辑
        tokens = text.split()  # 简单示例，使用空格进行分词
        return tokens

    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)

# 创建自定义标记器实例
tokenizer = CustomTokenizer(inputCol='text', outputCol='tokens')

# 使用自定义标记器对DataFrame进行转换
tokenized_df = tokenizer.transform(input_df)