Xinyong Tian created SPARK-25441:
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Summary: calculate term frequency in CountVectorizer()
Key: SPARK-25441
URL: https://issues.apache.org/jira/browse/SPARK-25441
Project: Spark
Issue Type: New Feature
Components: ML
Affects Versions: 2.3.1
Reporter: Xinyong Tian
currently CountVectorizer() can not output TF (term frequency). I hope there
will be such option.
TF defined as https://en.m.wikipedia.org/wiki/Tf–idf
example,
>>> df = spark.createDataFrame( ... [(0, ["a", "b", "c"]), (1, ["a", "b", "b",
>>> "c", "a"])], ... ["label", "raw"])
>>> cv = CountVectorizer(inputCol="raw", outputCol="vectors")
>>> model = cv.fit(df)
>>> model.transform(df).limit(1).show(truncate=False)
label raw vectors
0 [a, b, c] (3,[0,1,2],[1.0,1.0,1.0])
instead I want
0 [a, b, c] (3,[0,1,2],[0.33,0.33,0.33]) # ie, each vector
devided by by its sum, here 3, so
sum of new vector will 1,for every
row(document)
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