Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7121#discussion_r33748863
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -88,12 +88,25 @@ class MatrixFactorizationModel(
* @return RDD of Ratings.
*/
def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = {
- val users = userFeatures.join(usersProducts).map {
- case (user, (uFeatures, product)) => (product, (user, uFeatures))
- }
- users.join(productFeatures).map {
- case (product, ((user, uFeatures), pFeatures)) =>
- Rating(user, product, blas.ddot(uFeatures.length, uFeatures, 1,
pFeatures, 1))
+ val usersCount = usersProducts.keys.countApproxDistinct()
--- End diff --
Btw, if we really want to optimize number of passes. We can copy the code
from
https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/RDD.scala#L1170
and use one pass to count both columns.
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