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From imatiach-msft <...@git.apache.org>
Subject [GitHub] spark pull request #16571: [SPARK-19208][ML] MaxAbsScaler and MinMaxScaler a...
Date Fri, 13 Jan 2017 23:09:31 GMT
Github user imatiach-msft commented on a diff in the pull request:

    https://github.com/apache/spark/pull/16571#discussion_r96092078
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala ---
    @@ -70,14 +67,40 @@ class MaxAbsScaler @Since("2.0.0") (@Since("2.0.0") override val uid:
String)
       @Since("2.0.0")
       override def fit(dataset: Dataset[_]): MaxAbsScalerModel = {
         transformSchema(dataset.schema, logging = true)
    -    val input: RDD[OldVector] = dataset.select($(inputCol)).rdd.map {
    -      case Row(v: Vector) => OldVectors.fromML(v)
    -    }
    -    val summary = Statistics.colStats(input)
    --- End diff --
    
    is it the call to colStats and vector conversion that was so inefficient?  do you have
any performance numbers to justify the change, since it does make the code more complicated.


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