Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/17059#discussion_r103378267
--- Diff: mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
---
@@ -82,12 +82,20 @@ private[recommendation] trait ALSModelParams extends
Params with HasPredictionCo
* Attempts to safely cast a user/item id to an Int. Throws an exception
if the value is
* out of integer range.
*/
- protected val checkedCast = udf { (n: Double) =>
- if (n > Int.MaxValue || n < Int.MinValue) {
- throw new IllegalArgumentException(s"ALS only supports values in
Integer range for columns " +
- s"${$(userCol)} and ${$(itemCol)}. Value $n was out of Integer
range.")
- } else {
- n.toInt
+ protected val checkedCast = udf { (n: Any) =>
+ n match {
+ case v: Int => v // Avoid unnecessary casting
+ case v: Number =>
+ val intV = v.intValue()
+ // Checks if number within Int range and has no fractional part.
+ if (v.doubleValue == intV) {
--- End diff --
Sorry, I'm not sure if this is a good idea due to floating point
precision... the code above doesn't seem to do this check, it just calls toInt
-- however, if this is absolutely necessary, I would hope that we could give
the user some way to specify the Int range or precision. Also, if we are going
to go ahead with this change, then we should add some tests to verify the case
that the exception is thrown, but without some ability to specify the precision
I'm not sure if this is the correct thing to do (?).
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