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https://issues.apache.org/jira/browse/SPARK-44585?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean R. Owen resolved SPARK-44585.
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Fix Version/s: 3.5.0
4.0.0
3.4.2
Resolution: Fixed
Issue resolved by pull request 42207
[https://github.com/apache/spark/pull/42207]
> Fix warning condition in MLLib RankingMetrics ndcgAk
> ----------------------------------------------------
>
> Key: SPARK-44585
> URL: https://issues.apache.org/jira/browse/SPARK-44585
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 3.4.1
> Reporter: Guilhem Vuillier
> Assignee: Guilhem Vuillier
> Priority: Minor
> Fix For: 3.5.0, 4.0.0, 3.4.2
>
>
> The implementation of nDCG evaluation in MLLib with relevance score (added in
> 3.4.0, see https://issues.apache.org/jira/browse/SPARK-39446 and [pull
> request|https://github.com/apache/spark/pull/36843]) implements the following
> warning when the input data isn't correct: "# of ground truth set and # of
> relevance value set should be equal, check input data"
>
> The logic for raising warnings is faulty at the moment: it raises a warning
> when the following conditions are both true:
> # {{rel}} is empty
> # {{lab.size}} and {{rel.size}} are not equal.
>
> With the current logic, RankingMetrics will:
> * raise incorrect warning when a user is using it in the "binary" mode (i.e.
> no relevance values in the input)
> * not raise warning (that could be necessary) when the user is using it in
> the "non-binary" model (i.e. with relevance values in the input)
>
> The logic should be to raise a warning should be:
> # {{rel}} is *not empty*
> # {{lab.size}} and {{rel.size}} are not equal.
>
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