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zhengruifeng commented on SPARK-19208:
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[~mlnick] +1
I think we can create a private UDAF to compute the metrics.
And for users, we create a wrapper {{VectorSummarizer}} based on the internal UDAF.
In algorithms, we can use both the {{VectorSummarizer}} and the UDAF (if we need to groupby)
[~imatiach] [~josephkb] Is this OK?
> 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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