Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/9008#discussion_r41573997
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala ---
@@ -1211,4 +1212,34 @@ private[ml] object RandomForest extends Logging {
}
}
+ /**
+ * Inject the sample weight to sub-sample weights of the baggedPoints
+ */
+ private[impl] def reweightSubSampleWeights(
+ baggedTreePoints: RDD[BaggedPoint[TreePoint]]):
RDD[BaggedPoint[TreePoint]] = {
+ baggedTreePoints.map {bagged =>
+ val treePoint = bagged.datum
+ val adjustedSubSampleWeights = bagged.subsampleWeights.map(w => w *
treePoint.weight)
+ new BaggedPoint[TreePoint](treePoint, adjustedSubSampleWeights)
+ }
+ }
+
+ /**
+ * A thin adaptor to
[[org.apache.spark.mllib.tree.impl.DecisionTreeMetadata.buildMetadata]]
+ */
+ private[impl] def buildWeightedMetadata(
+ input: RDD[WeightedLabeledPoint],
+ strategy: OldStrategy,
+ numTrees: Int,
+ featureSubsetStrategy: String) = {
--- End diff --
Should specify return type here.
Is the reason that you can't just modify `buildMetadata` to accept and
`RDD[WeightedLabeledPoint]` because you are trying not to change MLlib
implementation?
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