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