在 PySpark ML 中创建自定义 Transformer

Question

我是 Spark SQL DataFrames 和 ML 的新手 (PySpark)。 如何创建自定义标记器，例如删除停用词并使用 nltk 中的一些库？我可以扩展默认的吗？

Answer 1

我可以扩展默认的吗？

不是真的。默认Tokenizer 是pyspark.ml.wrapper.JavaTransformer 的子类，并且与pyspark.ml.feature 中的其他转换器和估计器相同，将实际处理委托给其Scala 对应物。既然你想使用 Python，你应该直接扩展 pyspark.ml.pipeline.Transformer。

import nltk

from pyspark import keyword_only  ## < 2.0 -> pyspark.ml.util.keyword_only
from pyspark.ml import Transformer
from pyspark.ml.param.shared import HasInputCol, HasOutputCol, Param, Params, TypeConverters
# Available in PySpark >= 2.3.0 
from pyspark.ml.util import DefaultParamsReadable, DefaultParamsWritable  
from pyspark.sql.functions import udf
from pyspark.sql.types import ArrayType, StringType

class NLTKWordPunctTokenizer(
        Transformer, HasInputCol, HasOutputCol,
        # Credits https://stackoverflow.com/a/52467470
        # by https://stackoverflow.com/users/234944/benjamin-manns
        DefaultParamsReadable, DefaultParamsWritable):

    stopwords = Param(Params._dummy(), "stopwords", "stopwords",
                      typeConverter=TypeConverters.toListString)


    @keyword_only
    def __init__(self, inputCol=None, outputCol=None, stopwords=None):
        super(NLTKWordPunctTokenizer, self).__init__()
        self.stopwords = Param(self, "stopwords", "")
        self._setDefault(stopwords=[])
        kwargs = self._input_kwargs
        self.setParams(**kwargs)

    @keyword_only
    def setParams(self, inputCol=None, outputCol=None, stopwords=None):
        kwargs = self._input_kwargs
        return self._set(**kwargs)

    def setStopwords(self, value):
        return self._set(stopwords=list(value))

    def getStopwords(self):
        return self.getOrDefault(self.stopwords)

    # Required in Spark >= 3.0
    def setInputCol(self, value):
        """
        Sets the value of :py:attr:`inputCol`.
        """
        return self._set(inputCol=value)

    # Required in Spark >= 3.0
    def setOutputCol(self, value):
        """
        Sets the value of :py:attr:`outputCol`.
        """
        return self._set(outputCol=value)

    def _transform(self, dataset):
        stopwords = set(self.getStopwords())

        def f(s):
            tokens = nltk.tokenize.wordpunct_tokenize(s)
            return [t for t in tokens if t.lower() not in stopwords]

        t = ArrayType(StringType())
        out_col = self.getOutputCol()
        in_col = dataset[self.getInputCol()]
        return dataset.withColumn(out_col, udf(f, t)(in_col))

使用示例（来自ML - Features的数据）：

sentenceDataFrame = spark.createDataFrame([
  (0, "Hi I heard about Spark"),
  (0, "I wish Java could use case classes"),
  (1, "Logistic regression models are neat")
], ["label", "sentence"])

tokenizer = NLTKWordPunctTokenizer(
    inputCol="sentence", outputCol="words",  
    stopwords=nltk.corpus.stopwords.words('english'))

tokenizer.transform(sentenceDataFrame).show()

对于自定义 Python Estimator，请参阅 How to Roll a Custom Estimator in PySpark mllib

⚠ 此答案取决于内部 API，并且与 Spark 2.0.3、2.1.1、2.2.0 或更高版本 (SPARK-19348) 兼容。有关与以前 Spark 版本兼容的代码，请参阅revision 8。