Robert Dodier created SPARK-12815:
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Summary: 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
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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