[ https://issues.apache.org/jira/browse/SPARK-12815?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Robert Dodier updated SPARK-12815: ---------------------------------- Description: 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. was: 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. > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org