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https://issues.apache.org/jira/browse/SPARK-25441?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-25441.
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Resolution: Won't Fix
What you have there is already term frequency. If you want to normalize it to
some kind of term fraction, you can just make that transformation yourself.
> 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
> Priority: Major
>
> 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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