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https://issues.apache.org/jira/browse/SPARK-17400?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15466957#comment-15466957
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Nick Pentreath commented on SPARK-17400:
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Could you explain further why you want to min-max scale the data? Seems you
have some user-item interaction data? What is the nature of it exactly?
There may be other ways of scaling that data or dealing with it before fitting
ALS - e.g. use {{MaxAbsScaler}} which preserves sparsity, or perhaps the
{{Binarizer}}?
Alternatively, depending on the nature of the data, you can just leave it as is
and use ALS with the implicit form (where the user-item scores are "preference
strengths").
> 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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