Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/12660#discussion_r61102146
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala ---
@@ -512,6 +514,55 @@ class ALSSuite
assert(getFactors(model.userFactors) ===
getFactors(model2.userFactors))
assert(getFactors(model.itemFactors) ===
getFactors(model2.itemFactors))
}
+
+ test("StorageLevel param") {
+ // test invalid param values
+ intercept[IllegalArgumentException] {
+ new ALS().setIntermediateRDDStorageLevel("foo")
+ }
+ intercept[IllegalArgumentException] {
+ new ALS().setIntermediateRDDStorageLevel("NONE")
+ }
+ intercept[IllegalArgumentException] {
+ new ALS().setFinalRDDStorageLevel("foo")
+ }
+ // test StorageLevels
+ val sqlContext = this.sqlContext
+ import sqlContext.implicits._
+ val (ratings, _) = genExplicitTestData(numUsers = 2, numItems = 2,
rank = 1)
+ val data = ratings.toDF
+ val als = new ALS().setMaxIter(1)
+ als.fit(data)
+ val factorRDD = sc.getPersistentRDDs.collect {
--- End diff --
I hope my understanding is clear here, please correct me if not. We don't
have a (good) way to check the storage level for the `userFactors` and
`itemFactors` dataframes, so it's checking the storage level of the user
factors RDD from which the dataframe was created. Are these equivalent? I don't
know as much about the storage for dataframes.
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