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From "Craig Macdonald (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-19683) Support for libsvm-based learning-to-rank format
Date Tue, 21 Feb 2017 20:12:51 GMT
Craig Macdonald created SPARK-19683:
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             Summary: 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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