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https://issues.apache.org/jira/browse/SPARK-14409?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15883431#comment-15883431
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Roberto Mirizzi commented on SPARK-14409:
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[~mlnick] my implementation was conceptually close to what we already have for
the existing mllib. If you look at the example in
http://spark.apache.org/docs/latest/mllib-evaluation-metrics.html#ranking-systems
they do exactly what I do with goodThreshold parameter.
As you can see in my approach, I am using collect_list and windowing, and I
simply pass the Dataset to the evaluator, similar to what we have for other
evaluators in ml.
IMO, that's the approach that has continuity with other existing evaluators.
However, if you think we should also support array columns, we can add that too.
> 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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