Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15149#discussion_r80063167
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
---
@@ -200,22 +200,17 @@ abstract class ProbabilisticClassificationModel[
if (!isDefined(thresholds)) {
probability.argmax
} else {
- val thresholds: Array[Double] = getThresholds
- val probabilities = probability.toArray
+ val thresholds = getThresholds
var argMax = 0
var max = Double.NegativeInfinity
var i = 0
val probabilitySize = probability.size
while (i < probabilitySize) {
- if (thresholds(i) == 0.0) {
- max = Double.PositiveInfinity
+ // thresholds are all > 0, excepting that at most one may be 0
+ val scaled = probability(i) / thresholds(i)
+ if (scaled > max) {
--- End diff --
If `probability(i)` and `thresholds(i)` are both 0.0 here, we will have
`scaled = NaN`. Maybe we can break out of the loop early if we encounter a zero
threshold. BTW, this also begs the question of what the answer should be with
an infinitely low probability and an infinitely low threshold - but I'm totally
fine just predicting whatever threshold is zero in that case :D
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