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From "Craig Macdonald (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-19683) Support for libsvm-based learning-to-rank format
Date Wed, 22 Feb 2017 10:52:44 GMT

    [ https://issues.apache.org/jira/browse/SPARK-19683?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15877987#comment-15877987
] 

Craig Macdonald commented on SPARK-19683:
-----------------------------------------

One might argue that ranking tasks can be as prevalent regression or classification. There
are also a multitude of LTR datasets:
  ** MSLR: https://www.microsoft.com/en-us/research/project/mslr/
  ** LETOR: http://research.microsoft.com/en-us/um/beijing/projects/letor/
  ** Yahoo learning to rank challenge: https://webscope.sandbox.yahoo.com/catalog.php?datatype=c
  ** https://academy.yandex.ru/events/data_analysis/grant2009/

I'm happy to make this within a separate application, but my secondary comment was that given
it was a simple extension to the libsvm dataframe reader, I was disappointed about how many
private classes that libsvm used that could not be easily reused.

> 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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