[jira] [Updated] (SPARK-10385) Bivariate statistics as UDAFs

["Xiangrui Meng (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-10385?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xiangrui Meng updated SPARK-10385:
----------------------------------
    Target Version/s:   (was: 1.6.0)

> Bivariate statistics as UDAFs
> -----------------------------
>
>                 Key: SPARK-10385
>                 URL: https://issues.apache.org/jira/browse/SPARK-10385
>             Project: Spark
>          Issue Type: Umbrella
>          Components: ML, SQL
>            Reporter: Xiangrui Meng
>            Assignee: Burak Yavuz
>
> Similar to SPARK-10384, it would be nice to have bivariate statistics defined as UDAFs. This JIRA discuss general implementation and track subtasks. Bivariate statistics include:
> * continuous: covariance, Pearson's correlation, and Spearman's correlation
> * categorical: ??
> If we define them as UDAFs, it would be flexible to use them with DataFrames, e.g.,
> {code}
> df.groupBy("key").agg(corr("x", "y"))
> {code}



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