[ https://issues.apache.org/jira/browse/SPARK-19208?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
zhengruifeng updated SPARK-19208: --------------------------------- Description: 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. was: 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}} fail because OOM {{MultivariateOnlineSummarizer}} maintain 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 one 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. > MaxAbsScaler and MinMaxScaler are very inefficient > -------------------------------------------------- > > Key: SPARK-19208 > URL: https://issues.apache.org/jira/browse/SPARK-19208 > Project: Spark > Issue Type: Improvement > Components: ML > Reporter: zhengruifeng > Assignee: Apache Spark > Attachments: 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org