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

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-18808?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen updated SPARK-18808:
------------------------------
    Issue Type: Improvement  (was: Bug)

Agree, the private predict() method should accept a reference to a broadcast if possible, and transform should create that broadcast. Go ahead and try it.

> ml.KMeansModel.transform is very inefficient
> --------------------------------------------
>
>                 Key: SPARK-18808
>                 URL: https://issues.apache.org/jira/browse/SPARK-18808
>             Project: Spark
>          Issue Type: Improvement
>          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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