Github user mtustin-handy commented on a diff in the pull request:
https://github.com/apache/spark/pull/11982#discussion_r57529308
--- Diff: core/src/main/scala/org/apache/spark/partial/SumEvaluator.scala
---
@@ -56,9 +56,12 @@ private[spark] class SumEvaluator(totalOutputs: Int,
confidence: Double)
val confFactor = {
if (counter.count > 100) {
new NormalDistribution().inverseCumulativeProbability(1 - (1 -
confidence) / 2)
- } else {
+ } else if (counter.count > 1) {
val degreesOfFreedom = (counter.count - 1).toInt
new
TDistribution(degreesOfFreedom).inverseCumulativeProbability(1 - (1 -
confidence) / 2)
+ } else {
+ // No way to meaningfully estimate confidence, so we signal no
particular confidence interval
+ Double.PositiveInfinity
--- End diff --
I'm not sure I follow you. If `sumStdev` is `NaN` it really won't matter
what this value is. If it's any other value, multiplying by `Infinity` results
in `Infinity`, which I believe is what is desired.
`sumStdDev` will only be `NaN` if `sumVar` is negative. By the construction
of `sumVar`, I don't think that's possible.
```
scala> Double.PositiveInfinity * 2
res1: Double = Infinity
scala> Double.PositiveInfinity * 2.0
res2: Double = Infinity
scala> Double.PositiveInfinity * Double.PositiveInfinity
res3: Double = Infinity
scala> Double.PositiveInfinity * Double.NegativeInfinity
res4: Double = -Infinity
```
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