Create a custom Transformer in PySpark ML

Question

I am new to Spark SQL DataFrames and ML on them (PySpark). How can I create a costume tokenizer, which for example removes stop words and uses some libraries from nltk? Can I extend the default one?

Thanks.

Answer 1

Can I extend the default one?

Not really. Default Tokenizer is a subclass of pyspark.ml.wrapper.JavaTransformer and, same as other transfromers and estimators from pyspark.ml.feature, delegates actual processing to its Scala counterpart. Since you want to use Python you should extend pyspark.ml.pipeline.Transformer directly.

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))

Example usage (data from ML - Features):

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()

For custom Python Estimator see How to Roll a Custom Estimator in PySpark mllib

⚠ This answer depends on internal API and is compatible with Spark 2.0.3, 2.1.1, 2.2.0 or later (SPARK-19348). For code compatible with previous Spark versions please see revision 8.