WeichenXu123 commented on code in PR #37918:
URL: https://github.com/apache/spark/pull/37918#discussion_r974197580
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mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala:
##########
@@ -496,18 +499,23 @@ class ALSModel private[ml] (
.iterator.map { j => (srcId, dstIds(j), scores(j)) }
}
}
- }
- // We'll force the IDs to be Int. Unfortunately this converts IDs to Int
in the output.
- val topKAggregator = new TopByKeyAggregator[Int, Int, Float](num,
Ordering.by(_._2))
- val recs = ratings.as[(Int, Int,
Float)].groupByKey(_._1).agg(topKAggregator.toColumn)
- .toDF("id", "recommendations")
+ }.toDF(srcOutputColumn, dstOutputColumn, ratingColumn)
+
+ val aggFunc = CollectOrdered(struct(ratingColumn, dstOutputColumn).expr,
num, true)
+ .toAggregateExpression(false)
Review Comment:
I think we can define a spark sql function and wrap this part within the
function, like:
```
def collect_top_k(ratingColumn, outputColumn) = {
CollectOrdered(struct(ratingColumn, outputColumn).expr, num, true)
}
```
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