[jira] [Commented] (SPARK-18808) ml.KMeansModel.transform is very inefficient

["Michel Lemay (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-18808?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15736254#comment-15736254 ] 

Michel Lemay commented on SPARK-18808:
--------------------------------------

Subclassing/overriding/adding methods in KMeans/Model is a pain because of all the private stuff.  
I cannot even add methods implicitly because parentModel is private and I have no way of calling the proper method on it.  I've seen other JIRA complaining about that lack of flexibility as well.

Right now, the only option I have is to code brand new KMeans* from scratch.

> ml.KMeansModel.transform is very inefficient
> --------------------------------------------
>
>                 Key: SPARK-18808
>                 URL: https://issues.apache.org/jira/browse/SPARK-18808
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 2.0.2
>            Reporter: Michel Lemay
>
> The function ml.KMeansModel.transform will call the parentModel.predict(features) method on each row which in turns will normalize all clusterCenters from mllib.KMeansModel.clusterCentersWithNorm every time!
> This is a serious waste of resources!  In my profiling, clusterCentersWithNorm represent 99% of the sampling!  
> This should have been implemented with a broadcast variable as it is done in other functions like computeCost.



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