[jira] [Commented] (SPARK-18808) ml.KMeansModel.transform is very inefficient
[ https://issues.apache.org/jira/browse/SPARK-18808?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15758577#comment-15758577 ] Apache Spark commented on SPARK-18808: -- User 'srowen' has created a pull request for this issue: https://github.com/apache/spark/pull/16328 > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18808) ml.KMeansModel.transform is very inefficient
[ https://issues.apache.org/jira/browse/SPARK-18808?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15757839#comment-15757839 ] yuhao yang commented on SPARK-18808: [~FlamingMike] Are you interested in sending a fix? > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18808) ml.KMeansModel.transform is very inefficient
[ https://issues.apache.org/jira/browse/SPARK-18808?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
