Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/13624#discussion_r68205142
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTrees.scala
---
@@ -205,31 +205,31 @@ private[spark] object GradientBoostedTrees extends
Logging {
case _ => data
}
- val numIterations = trees.length
- val evaluationArray = Array.fill(numIterations)(0.0)
- val localTreeWeights = treeWeights
-
- var predictionAndError = computeInitialPredictionAndError(
- remappedData, localTreeWeights(0), trees(0), loss)
-
- evaluationArray(0) = predictionAndError.values.mean()
-
val broadcastTrees = sc.broadcast(trees)
- (1 until numIterations).foreach { nTree =>
- predictionAndError =
remappedData.zip(predictionAndError).mapPartitions { iter =>
- val currentTree = broadcastTrees.value(nTree)
- val currentTreeWeight = localTreeWeights(nTree)
- iter.map { case (point, (pred, error)) =>
- val newPred = updatePrediction(point.features, pred,
currentTree, currentTreeWeight)
- val newError = loss.computeError(newPred, point.label)
- (newPred, newError)
- }
+ val localTreeWeights = treeWeights
+ val treesIndices = trees.indices
+
+ val dataCount = remappedData.count()
+ val evaluation = remappedData
+ .map { (point: LabeledPoint) =>
+ treesIndices
+ .map(idx => {
+ val prediction = broadcastTrees.value(idx)
+ .rootNode
+ .predictImpl(point.features)
+ .prediction
+ prediction * localTreeWeights(idx)
--- End diff --
It might be very slightly more consistent to try to also use
`updatePrediction` here with a 0 current prediction, just to make it pro forma
consistent with the rest.
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