[jira] [Updated] (SPARK-4494) IDFModel.transform() add support for single vector

["Jean-Philippe Quemener (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-4494?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Jean-Philippe Quemener updated SPARK-4494:
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    Summary: IDFModel.transform() add support for single vector  (was: IDFModel.transform() add support for single vectors)

> IDFModel.transform() add support for single vector
> --------------------------------------------------
>
>                 Key: SPARK-4494
>                 URL: https://issues.apache.org/jira/browse/SPARK-4494
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Jean-Philippe Quemener
>
> For now when using the tfidf implementation in mllib you have no other possibility to map your data back onto i.e. labels or ids than use a hackish way with ziping: {quote} 1. Persist input RDD. 2. Transform it to just vectors and apply IDFModel 3. zip with original RDD 4. transform label and new vector to LabeledPoint{quote}
> Source:[http://stackoverflow.com/questions/26897908/spark-mllib-tfidf-implementation-for-logisticregression]
> I think as in production alot of users want to map their data back to some identifier, it would be a good imporvement to allow using single vectors on IDFModel.transform()



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