Question

我是Spark SQL DataFrames和ML的新手（PySpark）。如何创建服装标记器，例如删除停用词并使用nltk中的某些库？我可以延长默认值吗？

感谢。

Answer 1

我可以延长默认值吗？

不是真的。默认Tokenizer是pyspark.ml.wrapper.JavaTransformer的子类，与pyspark.ml.feature中的其他transfromers和估算器相同，将实际处理委托给其Scala对应项。由于您要使用Python，因此应直接扩展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
from pyspark.sql.functions import udf
from pyspark.sql.types import ArrayType, StringType

class NLTKWordPunctTokenizer(Transformer, HasInputCol, HasOutputCol):

    @keyword_only
    def __init__(self, inputCol=None, outputCol=None, stopwords=None):
        super(NLTKWordPunctTokenizer, self).__init__()
        self.stopwords = Param(self, "stopwords", "")
        self._setDefault(stopwords=set())
        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):
        self._paramMap[self.stopwords] = value
        return self

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

    def _transform(self, dataset):
        stopwords = 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))

示例用法（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=set(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，并与Spark 2.0.3,2.1.1,2.2.0或更高版本（SPARK-19348）兼容。有关与之前Spark版本兼容的代码，请参阅revision 8。

在PySpark ML中创建自定义Transformer

1 个答案: