Github user AnthonyTruchet commented on a diff in the pull request:
https://github.com/apache/spark/pull/16037#discussion_r91493798
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -241,16 +241,24 @@ object LBFGS extends Logging {
val bcW = data.context.broadcast(w)
val localGradient = gradient
- val (gradientSum, lossSum) = data.treeAggregate((Vectors.zeros(n),
0.0))(
- seqOp = (c, v) => (c, v) match { case ((grad, loss), (label,
features)) =>
- val l = localGradient.compute(
- features, label, bcW.value, grad)
- (grad, loss + l)
- },
- combOp = (c1, c2) => (c1, c2) match { case ((grad1, loss1),
(grad2, loss2)) =>
- axpy(1.0, grad2, grad1)
- (grad1, loss1 + loss2)
- })
+ val seqOp = (c: (Vector, Double), v: (Double, Vector)) =>
+ (c, v) match {
+ case ((grad, loss), (label, features)) =>
+ val denseGrad = grad.toDense
+ val l = localGradient.compute(features, label, bcW.value,
denseGrad)
+ (denseGrad, loss + l)
+ }
+
+ val combOp = (c1: (Vector, Double), c2: (Vector, Double)) =>
+ (c1, c2) match { case ((grad1, loss1), (grad2, loss2)) =>
+ val denseGrad1 = grad1.toDense
--- End diff --
I'm sorry, I have no clue how to generate a non-empty RDD with an empty
partition. Can you please hint me at some entry points so that I can contribute
the UTest you request ?
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