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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 01:31:21 GMT

    [ https://issues.apache.org/jira/browse/SPARK-17400?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15472382#comment-15472382
] 

Hadoop QA commented on SPARK-17400:
-----------------------------------


     [ https://issues.apache.org/jira/browse/SPARK-17400?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Frank Dai updated SPARK-17400:
------------------------------
    Description: 
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 

  was:
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





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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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