Github user MLnick commented on the issue:
https://github.com/apache/spark/pull/18554
I agree on this
On Tue, 11 Jul 2017 at 16:53, Yanbo Liang <notificati...@github.com> wrote:
> *@yanboliang* commented on this pull request.
> ------------------------------
>
> In mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
> <https://github.com/apache/spark/pull/18554#discussion_r126710934>:
>
> > @@ -317,7 +318,12 @@ final class OneVsRest @Since("1.4.0") (
> val numClasses =
MetadataUtils.getNumClasses(labelSchema).fold(computeNumClasses())(identity)
> instr.logNumClasses(numClasses)
>
> - val multiclassLabeled = dataset.select($(labelCol), $(featuresCol))
> + val multiclassLabeled = getClassifier match {
> + // SPARK-21306: cache weightCol if necessary
> + case c: HasWeightCol if c.isDefined(c.weightCol) &&
c.getWeightCol.nonEmpty =>
> + dataset.select($(labelCol), $(featuresCol), c.getWeightCol)
> + case _ => dataset.select($(labelCol), $(featuresCol))
> + }
>
> OneVsRest is a classification estimator, I think we should make weightCol
> a member param of it like featuresCol. For example:
>
> val dataset = dataset // This dataset has column: a, b, c.
> val ova = new OneVsRest().setFeaturesCol("a").setClassifier(new
LogisticRegression().setFeaturesCol("b"))
>
> The features column used by OneVsRest is a. The features column set for
> OneVsRest will override corresponding set in OneVsRest.classifier. So we
> should follow this way for weightCol as well. Thanks.
>
> â
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