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Joseph K. Bradley commented on SPARK-19208: ------------------------------------------- Thanks for writing out your ideas. Here are my thoughts about the API: *Reference API: Double column stats* When working with Double columns (not Vectors), one would expect write things like: {{myDataFrame.select(min("x"), max("x"))}} to select 2 stats, min and max. Here, min and max are functions provided by Spark SQL which return columns. *Analogy* We should probably provide an analogous API. Here's what I imagine: {code} import org.apache.spark.ml.stat.VectorSummary val df: DataFrame = ... val results: DataFrame = df.select(VectorSummary.min("features"), VectorSummary.mean("features")) val weightedResults: DataFrame = df.select(VectorSummary.min("features"), VectorSummary.mean("features", "weight")) // Both of these result DataFrames contain 2 Vector columns. {code} I.e., we provide vectorized versions of stats functions. If you want to put everything into a single function, then we could also have VectorSummary have a function "summary" which returns a struct type with every stat available: {code} val results = df.select(VectorSummary.summary("features", "weights")) // results DataFrame contains 1 struct column, which has a Vector field for every statistic we provide. {code} Note: I removed "online" from the name since it the user does not need to know that it does online aggregation. > 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. -- 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