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https://issues.apache.org/jira/browse/SPARK-14409?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15882163#comment-15882163
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Nick Pentreath commented on SPARK-14409:
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[~roberto.mirizzi] the {{goodThreshold}} param seems pretty reasonable in this
context to exclude irrelevant items. I think it can be a good {{expertParam}}
addition.
Ok, I think that a first pass at this should just aim to replicate what we have
exposed in {{mllib}} and wrap {{RankingMetrics}}. Initially we can look at: (a)
supporting numeric columns and doing the windowing & {{collect_list}} approach
to feed into {{RankingMetrics}}; (b) support Array columns and feed directly
into {{RankingMetrics}} or (c) support both.
[~yongtang] already did a PR here: https://github.com/apache/spark/pull/12461.
It is fairly complete and also includes MRR. [~yongtang] are you able to work
on reviving that PR? If os, [~roberto.mirizzi] [~danilo.ascione] are you able
to help review that PR?
> 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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