[jira] [Closed] (SPARK-19957) Inconsist KMeans initialization mode behavior between ML and MLlib

["yuhao yang (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-19957?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

yuhao yang closed SPARK-19957.
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    Resolution: Not A Problem

> Inconsist KMeans initialization mode behavior between ML and MLlib
> ------------------------------------------------------------------
>
>                 Key: SPARK-19957
>                 URL: https://issues.apache.org/jira/browse/SPARK-19957
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 2.1.0
>            Reporter: yuhao yang
>            Priority: Minor
>
> when users set the initialization mode to "random", KMeans in ML and MLlib has inconsistent behavior for multiple runs:
> MLlib will basically use new Random for each run.
> ML Kmeans however will use the default random seed, which is {code}this.getClass.getName.hashCode.toLong{code}, and keep using the same number among multiple fitting.
> I would expect the "random" initialization mode to be literally random. There're different solutions with different scope of impact. Adjusting the hasSeed trait may have a broader impact(but maybe worth discussion). We can always just set random default seed in KMeans. 
> Appreciate your feedback.



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