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https://issues.apache.org/jira/browse/SPARK-14409?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15245084#comment-15245084
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Yong Tang commented on SPARK-14409:
-----------------------------------
Thanks [~mlnick] for the references. I will take a look at those and see what
we could do with it.
By the way, initially I though I could easily calling RankingMetrics in
mllib.evaluation from the new ml.evaluation.RankingEvaluator. However, I am
having some trouble in implementation because the
`
@Since("2.0.0")
override def evaluate(dataset: Dataset[_]): Double
`
in `RankingEvaluator` is not so easy to be converted into RankingMetrics's
(`RDD[(Array[T], Array[T])]`).
I will do some further investigation. If I can not find a easy way to convert
the data set into generic `RDD[(Array[T], Array[T])]`, I will go directly
implementing the methods in new ml.evaluation (instead of calling
mllib.evaluation).
> 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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