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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-4494) IDFModel.transform() add support for single vector
Date Thu, 04 Dec 2014 10:10:12 GMT

    [ https://issues.apache.org/jira/browse/SPARK-4494?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14234091#comment-14234091
] 

Apache Spark commented on SPARK-4494:
-------------------------------------

User 'yu-iskw' has created a pull request for this issue:
https://github.com/apache/spark/pull/3603

> 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
>    Affects Versions: 1.1.1, 1.2.0
>            Reporter: Jean-Philippe Quemener
>            Priority: Minor
>
> For now when using the tfidf implementation of 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 a single vector on IDFModel.transform()



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