Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r20050413
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -135,8 +141,11 @@ object MovieLensALS {
println(s"Got $numRatings ratings from $numUsers users on $numMovies
movies.")
- val splits = ratings.randomSplit(Array(0.8, 0.2))
- val training = splits(0).cache()
+ val fractions = (0 until numUsers.toInt).map(x => (x + 1, 0.8)).toMap
+
+ val training = ratings.map { x => (x.user, x) }.sampleByKey(false,
fractions).map { x => x._2 }
--- End diff --
`sampleByKey` with the same `fraction` for each group is the same as
`sample(false, fraction)`. If you use `sampleByKeyExact`, we need to collect
the keys to driver, which is expensive. I think we can keep the simple
`randomSplit` code, and in evaluation, we use a outer join and define a metric
for those not appeared in training.
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