Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/16441#discussion_r94875629
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala ---
@@ -248,12 +269,38 @@ class GBTClassificationModel private[ml](
if (prediction > 0.0) 1.0 else 0.0
}
+ override protected def predictRaw(features: Vector): Vector = {
+ val treePredictions =
_trees.map(_.rootNode.predictImpl(features).prediction)
+ val prediction = blas.ddot(numTrees, treePredictions, 1, _treeWeights,
1)
--- End diff --
it looks like BLAS.dot is only for Vector, but these are both arrays. I'm
worried that this may degrade performance. Is this specifically what you are
looking for:
BLAS.dot(Vectors.dense(treePredictions), Vectors.dense(_treeWeights))
is the extra dense vector allocation worth it?
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