[jira] [Closed] (SPARK-17400) MinMaxScaler.transform() outputs DenseVector by default, which causes poor performance

["Frank Dai (JIRA)" <ji...@apache.org>]

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

Frank Dai closed SPARK-17400.
-----------------------------
    Resolution: Not A Problem

> 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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