Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/18624#discussion_r127443513
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -286,40 +288,120 @@ object MatrixFactorizationModel extends
Loader[MatrixFactorizationModel] {
srcFeatures: RDD[(Int, Array[Double])],
dstFeatures: RDD[(Int, Array[Double])],
num: Int): RDD[(Int, Array[(Int, Double)])] = {
- val srcBlocks = blockify(srcFeatures)
- val dstBlocks = blockify(dstFeatures)
- val ratings = srcBlocks.cartesian(dstBlocks).flatMap { case (srcIter,
dstIter) =>
- val m = srcIter.size
- val n = math.min(dstIter.size, num)
- val output = new Array[(Int, (Int, Double))](m * n)
+ val srcBlocks = blockify(rank, srcFeatures).zipWithIndex()
+ val dstBlocks = blockify(rank, dstFeatures)
+ val ratings = srcBlocks.cartesian(dstBlocks).map {
+ case (((srcIds, srcFactors), index), (dstIds, dstFactors)) =>
+ val m = srcIds.length
+ val n = dstIds.length
+ val dstIdMatrix = new Array[Int](m * num)
+ val scoreMatrix = Array.fill[Double](m *
num)(Double.NegativeInfinity)
+ val pq = new BoundedPriorityQueue[(Int,
Double)](num)(Ordering.by(_._2))
+
+ val ratings = srcFactors.transpose.multiply(dstFactors)
+ var i = 0
+ var j = 0
+ while (i < m) {
+ var k = 0
+ while (k < n) {
+ pq += dstIds(k) -> ratings(i, k)
+ k += 1
+ }
+ var size = pq.size
+ while (size > 0) {
+ size -= 1
+ val factor = pq.poll()
--- End diff --
Is it really necessary to add `poll`? For size of `k` (which is usually
very small), the approach of `pq.foreach` should suffice and is simpler
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