Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/17742#discussion_r113762883
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -276,18 +277,39 @@ object MatrixFactorizationModel extends
Loader[MatrixFactorizationModel] {
num: Int): RDD[(Int, Array[(Int, Double)])] = {
val srcBlocks = blockify(rank, srcFeatures)
val dstBlocks = blockify(rank, dstFeatures)
- val ratings = srcBlocks.cartesian(dstBlocks).flatMap {
- case ((srcIds, srcFactors), (dstIds, dstFactors)) =>
- val m = srcIds.length
- val n = dstIds.length
- val ratings = srcFactors.transpose.multiply(dstFactors)
- val output = new Array[(Int, (Int, Double))](m * n)
- var k = 0
- ratings.foreachActive { (i, j, r) =>
- output(k) = (srcIds(i), (dstIds(j), r))
- k += 1
- }
- output.toSeq
+ val ratings = srcBlocks.cartesian(dstBlocks).flatMap { case (srcIter,
dstIter) =>
--- End diff --
I'd like to more detail to the doc string comment for this method to
explain the approach used for efficiency.
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