Github user sueann commented on a diff in the pull request:
https://github.com/apache/spark/pull/17090#discussion_r104036563
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala ---
@@ -594,6 +595,95 @@ class ALSSuite
model.setColdStartStrategy(s).transform(data)
}
}
+
+ private def getALSModel = {
+ val spark = this.spark
+ import spark.implicits._
+
+ val userFactors = Seq(
+ (0, Array(6.0f, 4.0f)),
+ (1, Array(3.0f, 4.0f)),
+ (2, Array(3.0f, 6.0f))
+ ).toDF("id", "features")
+ val itemFactors = Seq(
+ (3, Array(5.0f, 6.0f)),
+ (4, Array(6.0f, 2.0f)),
+ (5, Array(3.0f, 6.0f)),
+ (6, Array(4.0f, 1.0f))
+ ).toDF("id", "features")
+ val als = new ALS().setRank(2)
+ new ALSModel(als.uid, als.getRank, userFactors, itemFactors)
+ .setUserCol("user")
+ .setItemCol("item")
+ }
+
+ test("recommendForAllUsers with k < num_items") {
+ val topItems = getALSModel.recommendForAllUsers(2)
+ assert(topItems.count() == 3)
+ assert(topItems.columns.contains("user"))
+
+ val expected = Map(
+ 0 -> Array(Row(3, 54f), Row(4, 44f)),
+ 1 -> Array(Row(3, 39f), Row(5, 33f)),
+ 2 -> Array(Row(3, 51f), Row(5, 45f))
+ )
+ checkRecommendations(topItems, expected, "item")
+ }
+
+ test("recommendForAllUsers with k = num_items") {
+ val topItems = getALSModel.recommendForAllUsers(4)
+ assert(topItems.count() == 3)
+ assert(topItems.columns.contains("user"))
+
+ val expected = Map(
+ 0 -> Array(Row(3, 54f), Row(4, 44f), Row(5, 42f), Row(6, 28f)),
+ 1 -> Array(Row(3, 39f), Row(5, 33f), Row(4, 26f), Row(6, 16f)),
+ 2 -> Array(Row(3, 51f), Row(5, 45f), Row(4, 30f), Row(6, 18f))
+ )
+ checkRecommendations(topItems, expected, "item")
+ }
+
+ test("recommendForAllItems with k < num_users") {
+ val topUsers = getALSModel.recommendForAllItems(2)
+ assert(topUsers.count() == 4)
+ assert(topUsers.columns.contains("item"))
+
+ val expected = Map(
+ 3 -> Array(Row(0, 54f), Row(2, 51f)),
+ 4 -> Array(Row(0, 44f), Row(2, 30f)),
+ 5 -> Array(Row(2, 45f), Row(0, 42f)),
+ 6 -> Array(Row(0, 28f), Row(2, 18f))
+ )
+ checkRecommendations(topUsers, expected, "user")
+ }
+
+ test("recommendForAllItems with k = num_users") {
+ val topUsers = getALSModel.recommendForAllItems(3)
+ assert(topUsers.count() == 4)
+ assert(topUsers.columns.contains("item"))
+
+ val expected = Map(
+ 3 -> Array(Row(0, 54f), Row(2, 51f), Row(1, 39f)),
+ 4 -> Array(Row(0, 44f), Row(2, 30f), Row(1, 26f)),
+ 5 -> Array(Row(2, 45f), Row(0, 42f), Row(1, 33f)),
+ 6 -> Array(Row(0, 28f), Row(2, 18f), Row(1, 16f))
+ )
+ checkRecommendations(topUsers, expected, "user")
+ }
+
+ private def checkRecommendations(
+ topK: DataFrame,
+ expected: Map[Int, Array[Row]],
+ dstColName: String): Unit = {
+ assert(topK.columns.contains("recommendations"))
+ topK.collect().foreach { row =>
+ val id = row.getInt(0)
+ val recs = row.getAs[WrappedArray[Row]]("recommendations")
+ assert(recs === expected(id))
+ assert(recs(0).fieldIndex(dstColName) == 0)
+ assert(recs(0).fieldIndex("rating") == 1)
--- End diff --
Actually nevermind. Either way is committing to an incompatible API so the
name one seems preferable.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
