srowen commented on a change in pull request #26454: [SPARK-29818][MLLIB]
Missing persist on RDD
URL: https://github.com/apache/spark/pull/26454#discussion_r345924649
##########
File path:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
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@@ -165,13 +166,17 @@ class BinaryClassificationMetrics @Since("3.0.0") (
confusions: RDD[(Double, BinaryConfusionMatrix)]) = {
// Create a bin for each distinct score value, count weighted positives and
// negatives within each bin, and then sort by score values in descending
order.
- val counts = scoreLabelsWeight.combineByKey(
+ val binnedWeights = scoreLabelsWeight.combineByKey(
createCombiner = (labelAndWeight: (Double, Double)) =>
new BinaryLabelCounter(0.0, 0.0) += (labelAndWeight._1,
labelAndWeight._2),
mergeValue = (c: BinaryLabelCounter, labelAndWeight: (Double, Double)) =>
c += (labelAndWeight._1, labelAndWeight._2),
mergeCombiners = (c1: BinaryLabelCounter, c2: BinaryLabelCounter) => c1
+= c2
- ).sortByKey(ascending = false)
+ )
+ if (scoreLabelsWeight.getStorageLevel != StorageLevel.NONE) {
+ binnedWeights.persist()
+ }
+ val counts = binnedWeights.sortByKey(ascending = false)
Review comment:
Doesn't seem so. But that is the question I'd put to you in these cases -
are you sure it makes a difference meaningful enough to overcome the overhead?
I could imagine so here, just wondering if these are based on more
investigation or benchmarking, vs just trying to persist lots of things.
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