[jira] [Comment Edited] (SPARK-14409) Investigate adding a RankingEvaluator to ML

["Yong Tang (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-14409?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15245084#comment-15245084 ] 

Yong Tang edited comment on SPARK-14409 at 4/18/16 3:12 AM:
------------------------------------------------------------

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 
{code}
  @Since("2.0.0")
  override def evaluate(dataset: Dataset[_]): Double
{code}
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).


was (Author: yongtang):
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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