[jira] [Resolved] (SPARK-19313) GaussianMixture throws cryptic error when number of features is too high

["Yanbo Liang (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-19313?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Yanbo Liang resolved SPARK-19313.
---------------------------------
       Resolution: Fixed
         Assignee: Seth Hendrickson
    Fix Version/s: 2.2.0

> GaussianMixture throws cryptic error when number of features is too high
> ------------------------------------------------------------------------
>
>                 Key: SPARK-19313
>                 URL: https://issues.apache.org/jira/browse/SPARK-19313
>             Project: Spark
>          Issue Type: Bug
>          Components: ML, MLlib
>            Reporter: Seth Hendrickson
>            Assignee: Seth Hendrickson
>            Priority: Minor
>             Fix For: 2.2.0
>
>
> The following fails
> {code}
>     val df = Seq(
>       Vectors.sparse(46400, Array(0, 4), Array(3.0, 8.0)),
>       Vectors.sparse(46400, Array(1, 5), Array(4.0, 9.0)))
>       .map(Tuple1.apply).toDF("features")
>     val gm = new GaussianMixture()
>     gm.fit(df)
> {code}
> It fails because GMMs allocate an array of size {{numFeatures * numFeatures}} and in this case we'll get integer overflow. We should limit the number of features appropriately.



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