Source code for pyspark.ml.feature

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from pyspark.ml.param.shared import HasInputCol, HasOutputCol, HasNumFeatures
from pyspark.ml.util import keyword_only
from pyspark.ml.wrapper import JavaTransformer
from pyspark.mllib.common import inherit_doc

__all__ = ['Tokenizer', 'HashingTF']


@inherit_doc
[docs]class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol): """ A tokenizer that converts the input string to lowercase and then splits it by white spaces. >>> from pyspark.sql import Row >>> df = sc.parallelize([Row(text="a b c")]).toDF() >>> tokenizer = Tokenizer(inputCol="text", outputCol="words") >>> print tokenizer.transform(df).head() Row(text=u'a b c', words=[u'a', u'b', u'c']) >>> # Change a parameter. >>> print tokenizer.setParams(outputCol="tokens").transform(df).head() Row(text=u'a b c', tokens=[u'a', u'b', u'c']) >>> # Temporarily modify a parameter. >>> print tokenizer.transform(df, {tokenizer.outputCol: "words"}).head() Row(text=u'a b c', words=[u'a', u'b', u'c']) >>> print tokenizer.transform(df).head() Row(text=u'a b c', tokens=[u'a', u'b', u'c']) >>> # Must use keyword arguments to specify params. >>> tokenizer.setParams("text") Traceback (most recent call last): ... TypeError: Method setParams forces keyword arguments. """ _java_class = "org.apache.spark.ml.feature.Tokenizer" @keyword_only def __init__(self, inputCol="input", outputCol="output"): """ __init__(self, inputCol="input", outputCol="output") """ super(Tokenizer, self).__init__() kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only
[docs] def setParams(self, inputCol="input", outputCol="output"): """ setParams(self, inputCol="input", outputCol="output") Sets params for this Tokenizer. """ kwargs = self.setParams._input_kwargs return self._set_params(**kwargs)
@inherit_doc
[docs]class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures): """ Maps a sequence of terms to their term frequencies using the hashing trick. >>> from pyspark.sql import Row >>> df = sc.parallelize([Row(words=["a", "b", "c"])]).toDF() >>> hashingTF = HashingTF(numFeatures=10, inputCol="words", outputCol="features") >>> print hashingTF.transform(df).head().features (10,[7,8,9],[1.0,1.0,1.0]) >>> print hashingTF.setParams(outputCol="freqs").transform(df).head().freqs (10,[7,8,9],[1.0,1.0,1.0]) >>> params = {hashingTF.numFeatures: 5, hashingTF.outputCol: "vector"} >>> print hashingTF.transform(df, params).head().vector (5,[2,3,4],[1.0,1.0,1.0]) """ _java_class = "org.apache.spark.ml.feature.HashingTF" @keyword_only def __init__(self, numFeatures=1 << 18, inputCol="input", outputCol="output"): """ __init__(self, numFeatures=1 << 18, inputCol="input", outputCol="output") """ super(HashingTF, self).__init__() kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only
[docs] def setParams(self, numFeatures=1 << 18, inputCol="input", outputCol="output"): """ setParams(self, numFeatures=1 << 18, inputCol="input", outputCol="output") Sets params for this HashingTF. """ kwargs = self.setParams._input_kwargs return self._set_params(**kwargs)
if __name__ == "__main__": import doctest from pyspark.context import SparkContext from pyspark.sql import SQLContext globs = globals().copy() # The small batch size here ensures that we see multiple batches, # even in these small test examples: sc = SparkContext("local[2]", "ml.feature tests") sqlContext = SQLContext(sc) globs['sc'] = sc globs['sqlContext'] = sqlContext (failure_count, test_count) = doctest.testmod( globs=globs, optionflags=doctest.ELLIPSIS) sc.stop() if failure_count: exit(-1)