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From "Hadoop QA (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-17400) MinMaxScaler.transform() outputs DenseVector by default, which causes poor performance
Date Thu, 08 Sep 2016 10:24:22 GMT

    [ https://issues.apache.org/jira/browse/SPARK-17400?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15473488#comment-15473488
] 

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)




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> 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.



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