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