You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Craig Macdonald (JIRA)" <ji...@apache.org> on 2017/02/21 20:12:51 UTC
[jira] [Created] (SPARK-19683) Support for libsvm-based
learning-to-rank format
Craig Macdonald created SPARK-19683:
---------------------------------------
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?
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org