[jira] [Commented] (SPARK-19683) Support for libsvm-based learning-to-rank format

["Sean Owen (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-19683?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15876812#comment-15876812 ] 

Sean Owen commented on SPARK-19683:
-----------------------------------

This is trivial for an application to implement. Unless it is a common format extension and one the core library can use, is there much particular reason for it to be in Spark vs app or external lib?

> Support for libsvm-based learning-to-rank format
> ------------------------------------------------
>
>                 Key: SPARK-19683
>                 URL: https://issues.apache.org/jira/browse/SPARK-19683
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML, MLlib
>    Affects Versions: 2.1.0
>            Reporter: Craig Macdonald
>            Priority: Minor
>
> I would like to use Spark for reading/processing Learning to Rank files. The standard format is an extension of libsvm:
> {code}
> 0 qid:1 1:2.9 2:9.4 # docid=clueweb09-00-01492
> {code}
> Under the mlib API, a LabeledPoint would need an extension called QueryLabeledPoint.
> I would also like to investigate use through the DataFrame, extending the libsvm source, however many of the classes/methods used there are private (e.g. LibSVMOptions, Datatype.sameType(), VectorUDT). So would an extension to handle LTR format be better inside Spark or outside?



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