[GitHub] spark pull request #12370: [SPARK-14599][ML] BaggedPoint should support samp...

[MechCoder <gi...@git.apache.org>]

Github user MechCoder commented on a diff in the pull request:

    https://github.com/apache/spark/pull/12370#discussion_r65994490
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/tree/impl/BaggedPoint.scala ---
    @@ -33,13 +33,20 @@ import org.apache.spark.util.random.XORShiftRandom
      * this datum has 1 copy, 0 copies, and 4 copies in the 3 subsamples, respectively.
      *
      * @param datum  Data instance
    - * @param subsampleWeights  Weight of this instance in each subsampled dataset.
    - *
    - * TODO: This does not currently support (Double) weighted instances.  Once MLlib has weighted
    - *       dataset support, update.  (We store subsampleWeights as Double for this future extension.)
    + * @param subsampleCounts  Number of samples of this instance in each subsampled dataset.
    + * @param sampleWeight The weight of this instance.
      */
    -private[spark] class BaggedPoint[Datum](val datum: Datum, val subsampleWeights: Array[Double])
    -  extends Serializable
    +private[spark] class BaggedPoint[Datum](
    +    val datum: Datum,
    +    val subsampleCounts: Array[Int],
    +    val sampleWeight: Double) extends Serializable {
    +
    +  /**
    +   * Subsample counts weighted by the sample weight.
    +   */
    +  def weightedCounts: Array[Double] = subsampleCounts.map(_ * sampleWeight)
    --- End diff --
    
    Should this be a `val`?


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