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From "Nick Pentreath (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (SPARK-19208) MultivariateOnlineSummarizer performance optimization
Date Wed, 01 Feb 2017 08:09:52 GMT

    [ https://issues.apache.org/jira/browse/SPARK-19208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15848108#comment-15848108
] 

Nick Pentreath edited comment on SPARK-19208 at 2/1/17 8:09 AM:
----------------------------------------------------------------

Another option would be an "Estimator" like API, where the UDAF is purely internal to the
API and not exposed to users, e.g.

{code}
val summarizer = new VectorSummarizer().setMetrics("min", "max").setInputCol("features").setWeightCol("weight")
val summary = summarizer.fit(df)

(or summarizer.evaluate, summarizer.summarize, etc?)

// this would need to throw exceptions (or perhaps return empty vectors) if the metric was
not set
val min: Vector = summary.getMin

// OR DataFrame-based result:

val min: Vector = summary.select("min").as[Vector]
{code}

Agree it is important (and the point of this issue) to (a) only compute required metrics;
and (b) not duplicate computation for efficiency.


was (Author: mlnick):
Another option would be an "Estimator" like API, where the UDAF is purely internal to the
API and not exposed to users, e.g.

{code}
val summarizer = new VectorSummarizer().setMetrics("min", "max").setInputCol("features").setWeightCol("weight")
val summary = summarizer.fit(df)

(or summarizer.evaluate, summarizer.summarize, etc?)

// this would need to throw exceptions (or perhaps return empty vectors) if the metric was
not set
val min: Vector = summary.getMin

// OR DataFrame-based result:

val min: Vector = summary.select("min").as[Vector]
{code}

> 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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