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

2017-03-15 Thread yuhao yang (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-19957?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15926404#comment-15926404
 ] 

yuhao yang commented on SPARK-19957:


Thanks for the response.

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

2017-03-15 Thread Sean Owen (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-19957?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15925753#comment-15925753
 ] 

Sean Owen commented on SPARK-19957:
---

Yeah I think this might be "working as intended".

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

2017-03-15 Thread Nick Pentreath (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-19957?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15925723#comment-15925723
 ] 

Nick Pentreath commented on SPARK-19957:


See https://issues.apache.org/jira/browse/SPARK-16832 

> 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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