Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15149#discussion_r80072011
--- 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 --
Ok, I think that's reasonable behavior. Is it better to handle the zero
threshold case in the code explicitly? It confused me at first, and I had to
refer to divide by zero behavior in scala to understand the code.
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