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Hadoop QA commented on SPARK-17400: ----------------------------------- [ https://issues.apache.org/jira/browse/SPARK-17400?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15464360#comment-15464360 ] Nick Pentreath edited comment on SPARK-17400 at 9/5/16 7:42 AM: ---------------------------------------------------------------- Can you comment more on the performance issue - are you actually seeing this in practice? From the comment, it seems in most cases zeros in the input vector would be transformed to non-zeros, so I wonder how much benefit is gained from a sparse representation? In any case, it seems like a fairly easy possible win to use {{SparseVector.compressed}} here (e.g. see https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala#L91) was (Author: mlnick): Can you comment more on the performance issue - are you actually seeing this in practice? From the comment, it seems in most cases zeros in the input vector would be transformed to non-zeros, so I wonder how much benefit is gained from a sparse representation? In any case, it seems like a fairly easy possible win to use `SparseVector.compressed` here (e.g. see https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala#L91) -- 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 --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org > MinMaxScaler.transform() outputs DenseVector by default, which causes poor > performance > -------------------------------------------------------------------------------------- > > Key: SPARK-17400 > URL: https://issues.apache.org/jira/browse/SPARK-17400 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib > Affects Versions: 1.6.1, 1.6.2, 2.0.0 > Reporter: Frank Dai > > MinMaxScaler.transform() outputs DenseVector by default, which will cause > poor performance and consume a lot of memory. > The most important line of code is the following: > https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala#L195 > I suggest that the code should calculate the number of non-zero elements in > advance, if the number of non-zero elements is less than half of the total > elements in the matrix, use SparseVector, otherwise use DenseVector > Or we can make it configurable by adding a parameter to > MinMaxScaler.transform(), for example MinMaxScaler.transform(isDense: > Boolean), so that users can decide whether their output result is dense or > sparse. -- 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