Skip to content

[SPARK-20596][ML][TEST] Consolidate and improve ALS recommendAll test cases #17860

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
MLnick wants to merge 1 commit into from
Closed

[SPARK-20596][ML][TEST] Consolidate and improve ALS recommendAll test cases #17860

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
63 changes: 25 additions & 38 deletions mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -671,58 +671,45 @@ class ALSSuite
.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((3, 54f), (4, 44f)),
1 -> Array((3, 39f), (5, 33f)),
2 -> Array((3, 51f), (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"))

test("recommendForAllUsers with k <, = and > num_items") {
val model = getALSModel
val numUsers = model.userFactors.count
val numItems = model.itemFactors.count
val expected = Map(
0 -> Array((3, 54f), (4, 44f), (5, 42f), (6, 28f)),
1 -> Array((3, 39f), (5, 33f), (4, 26f), (6, 16f)),
2 -> Array((3, 51f), (5, 45f), (4, 30f), (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((0, 54f), (2, 51f)),
4 -> Array((0, 44f), (2, 30f)),
5 -> Array((2, 45f), (0, 42f)),
6 -> Array((0, 28f), (2, 18f))
)
checkRecommendations(topUsers, expected, "user")
Seq(2, 4, 6).foreach { k =>
val n = math.min(k, numItems).toInt
val expectedUpToN = expected.mapValues(_.slice(0, n))
val topItems = model.recommendForAllUsers(k)
assert(topItems.count() == numUsers)
assert(topItems.columns.contains("user"))
checkRecommendations(topItems, expectedUpToN, "item")
}
}

test("recommendForAllItems with k = num_users") {
val topUsers = getALSModel.recommendForAllItems(3)
assert(topUsers.count() == 4)
assert(topUsers.columns.contains("item"))

test("recommendForAllItems with k <, = and > num_users") {
val model = getALSModel
val numUsers = model.userFactors.count
val numItems = model.itemFactors.count
val expected = Map(
3 -> Array((0, 54f), (2, 51f), (1, 39f)),
4 -> Array((0, 44f), (2, 30f), (1, 26f)),
5 -> Array((2, 45f), (0, 42f), (1, 33f)),
6 -> Array((0, 28f), (2, 18f), (1, 16f))
)
checkRecommendations(topUsers, expected, "user")

Seq(2, 3, 4).foreach { k =>
val n = math.min(k, numUsers).toInt
val expectedUpToN = expected.mapValues(_.slice(0, n))
val topUsers = getALSModel.recommendForAllItems(k)
assert(topUsers.count() == numItems)
assert(topUsers.columns.contains("item"))
checkRecommendations(topUsers, expectedUpToN, "user")
}
}

private def checkRecommendations(
Expand Down