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From "Nick Pentreath (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-19208) MultivariateOnlineSummarizer performance optimization
Date Tue, 14 Feb 2017 21:07:41 GMT

    [ https://issues.apache.org/jira/browse/SPARK-19208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15866675#comment-15866675
] 

Nick Pentreath commented on SPARK-19208:
----------------------------------------

When I said "estimator-like", I didn't mean it should necessarily be an actual {{Estimator}}
(I agree it is not really intended to fit into transformers & pipelines), but rather mimic
the API, i.e. that the summarizer is "fitted" on a dataset to return a summary.

I just wasn't too keen on the idea of returning a struct as it just feels sort of clunky relative
to returning a df with vector columns {{"mean", "min", "max"}} etc.

Supporting SS and {{groupBy}} seems like an important goal, so something like [~timhunter]'s
suggestion looks like it will work nicely.

For doing it via catalyst rules, that would be first prize to automatically re-use the intermediate
results for multiple end-result computations, and only compute what is necessary for the required
end-results. But, is that even supported for UDTs currently? I'm not an expert but my understanding
was that is not supported yet.

> MultivariateOnlineSummarizer performance optimization
> -----------------------------------------------------
>
>                 Key: SPARK-19208
>                 URL: https://issues.apache.org/jira/browse/SPARK-19208
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>            Reporter: zhengruifeng
>         Attachments: Tests.pdf, WechatIMG2621.jpeg
>
>
> Now, {{MaxAbsScaler}} and {{MinMaxScaler}} are using {{MultivariateOnlineSummarizer}}
to compute the min/max.
> However {{MultivariateOnlineSummarizer}} will also compute extra unused statistics. It
slows down the task, moreover it is more prone to cause OOM.
> For example:
> env : --driver-memory 4G --executor-memory 1G --num-executors 4
> data: [http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#kdd2010%20(bridge%20to%20algebra)]
748401 instances,   and 29,890,095 features
> {{MaxAbsScaler.fit}} fails because of OOM
> {{MultivariateOnlineSummarizer}} maintains 8 arrays:
> {code}
> private var currMean: Array[Double] = _
>   private var currM2n: Array[Double] = _
>   private var currM2: Array[Double] = _
>   private var currL1: Array[Double] = _
>   private var totalCnt: Long = 0
>   private var totalWeightSum: Double = 0.0
>   private var weightSquareSum: Double = 0.0
>   private var weightSum: Array[Double] = _
>   private var nnz: Array[Long] = _
>   private var currMax: Array[Double] = _
>   private var currMin: Array[Double] = _
> {code}
> For {{MaxAbsScaler}}, only 1 array is needed (max of abs value)
> For {{MinMaxScaler}}, only 3 arrays are needed (max, min, nnz)
> After modication in the pr, the above example run successfully.



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