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