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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2014/12/15 22:45:14 UTC
[jira] [Updated] (SPARK-4494) IDFModel.transform() add support for
single vector
[ https://issues.apache.org/jira/browse/SPARK-4494?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiangrui Meng updated SPARK-4494:
---------------------------------
Assignee: Yu Ishikawa
> 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
> Assignee: Yu Ishikawa
> Priority: Minor
> Fix For: 1.3.0
>
>
> 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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