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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:38:10 UTC
[jira] [Resolved] (SPARK-12815) Compute Wilcoxon-Mann-Whitney rank
sum statistic
[ https://issues.apache.org/jira/browse/SPARK-12815?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-12815.
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Resolution: Incomplete
> Compute Wilcoxon-Mann-Whitney rank sum statistic
> ------------------------------------------------
>
> Key: SPARK-12815
> URL: https://issues.apache.org/jira/browse/SPARK-12815
> Project: Spark
> Issue Type: New Feature
> Components: ML, MLlib
> Reporter: Robert Dodier
> Priority: Minor
> Labels: bulk-closed
>
> The Wilcoxon-Mann-Whitney rank sum statistic (also known by other permutations of those names) is a useful assessment of relevance of an input field for a classification problem. As such it would nice to have in ML or MLlib (I don't know what's a more suitable package for it).
> I have created a Spark package, [spark-wilcoxon|http://spark-packages.org/package/robert-dodier/spark-wilcoxon], to demonstrate an implementation. If there is interest in this issue, I'll create a pull request. spark-wilcoxon computes the scaled rank sum statistic {{U/(n0*n1)}}, where {{U}} is the rank sum statistic and {{n0}} and {{n1}} are the numbers of data in class 0 and class 1, respectively.
> There exists already the Spearman rank correlation statistic in MLlib (in ...mllib.stat.correlation.SpearmanCorrelation) but that is not equivalent to the WMW statistic -- the one cannot be derived from the other because the Spearman correlation contains squares of rank differences and the WMW contains only first-order terms.
> See the Wikipedia article [Mann-Whitney U test|https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test] for formulas and background information. At this point, I am proposing only to compute the rank sum statistic, not to implement the significance test.
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