Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/12762#discussion_r64868978
--- Diff: mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
---
@@ -242,16 +263,19 @@ class ALSModel private[ml] (
}
}
dataset
- .join(userFactors, dataset($(userCol)) === userFactors("id"), "left")
- .join(itemFactors, dataset($(itemCol)) === itemFactors("id"), "left")
+ .join(userFactors,
+ checkedCast(dataset($(userCol)).cast(DoubleType)) ===
userFactors("id"), "left")
--- End diff --
@jkbradley the existing code did the cast to `Int` - that means passing in
`Long` or `Double` (say) would silently cast and potentially lose precison and
give weird results, with no exception or warning. That's why here we cast to
`DoubleType` and use the `checkedCast` `udf` to do a safe cast _if_ the value
is within `Integer` range. If not we throw an exception with a helpful message.
This is so we allow can any numeric type for the user/item columns
(providing some form of "backward compatability" with the old version that
didn't check types at all), but we can still only support actual _values_ that
are `Int`s.
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