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https://issues.apache.org/jira/browse/SPARK-14409?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15825707#comment-15825707
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Danilo Ascione commented on SPARK-14409:
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I have implemented a Dataframe api based RankingEvaluator that can be used in
model selection pipeline (Cross-Validation).
The approach is similar to that of [~roberto.mirizzi].
I posted some usage code in
[SPARK-13857|https://issues.apache.org/jira/browse/SPARK-13857?focusedCommentId=15822021&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15822021]
for discussion.
Code is here
https://github.com/daniloascione/spark/commit/c93ab86d35984e9f70a3b4f543fb88f5541333f0
I would appreciate some feedback. Thanks!
> Investigate adding a RankingEvaluator to ML
> -------------------------------------------
>
> Key: SPARK-14409
> URL: https://issues.apache.org/jira/browse/SPARK-14409
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Nick Pentreath
> Priority: Minor
>
> {{mllib.evaluation}} contains a {{RankingMetrics}} class, while there is no
> {{RankingEvaluator}} in {{ml.evaluation}}. Such an evaluator can be useful
> for recommendation evaluation (and can be useful in other settings
> potentially).
> Should be thought about in conjunction with adding the "recommendAll" methods
> in SPARK-13857, so that top-k ranking metrics can be used in cross-validators.
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