Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14643#discussion_r74732778
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
---
@@ -201,11 +201,18 @@ abstract class ProbabilisticClassificationModel[
probability.argmax
} else {
val thresholds: Array[Double] = getThresholds
- val scaledProbability: Array[Double] =
- probability.toArray.zip(thresholds).map { case (p, t) =>
- if (t == 0.0) Double.PositiveInfinity else p / t
- }
- Vectors.dense(scaledProbability).argmax
+
+ if (thresholds.contains(0.0)) {
+ val indices = thresholds.zipWithIndex.filter(_._1 == 0.0).map(_._2)
+ val values = indices.map(probability.apply)
+ Vectors.sparse(numClasses, indices, values).argmax
+ } else {
+ val scaledProbability: Array[Double] =
+ probability.toArray.zip(thresholds).map { case (p, t) =>
+ if (t == 0.0) Double.PositiveInfinity else p / t
--- End diff --
You don't need the check for 0.0 in this case right?
I see the logic of the change, but I wonder what the motivation is for
computing a ratio of probabilities here to begin with? I wonder if K-L
divergence makes sense here instead: `p*math.log(p/t) +
(1-p)*math.log((1-p)/(1-t))`
@holdenk I believe you added this bit in
https://github.com/zhengruifeng/spark/commit/5a23213c148bfe362514f9c71f5273ebda0a848a#diff-4a7c1a2a6f2706d045b694f6d7a054f1R201
?
As you can see that formula would imply that t > 0 and t < 1. t == 0 means
"always predict this", and t == 1 means "never predict this". t == 1 is easy
enough to filter out from consideration, though maybe args checking should
catch the case that all thresholds are 1 earlier on.
t == 0 is valid if it's true of just one class. That too can be checked
when it's specified. If that's the case, of course the class to predict is
clear. Otherwise K-L applies.
I wonder if it's worth expanding scope to consider this?
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