Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/15593#discussion_r86420010
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -1486,57 +1489,75 @@ private class LogisticAggregator(
var marginOfLabel = 0.0
var maxMargin = Double.NegativeInfinity
- val margins = Array.tabulate(numClasses) { i =>
- var margin = 0.0
- features.foreachActive { (index, value) =>
- if (localFeaturesStd(index) != 0.0 && value != 0.0) {
- margin += localCoefficients(i * numFeaturesPlusIntercept +
index) *
- value / localFeaturesStd(index)
- }
+ val margins = new Array[Double](numClasses)
+ features.foreachActive { (index, value) =>
+ val stdValue = value / localFeaturesStd(index)
+ var j = 0
+ while (j < numClasses) {
+ margins(j) += localCoefficients(index * numClasses + j) * stdValue
+ j += 1
}
-
+ }
+ var i = 0
+ while (i < numClasses) {
if (fitIntercept) {
- margin += localCoefficients(i * numFeaturesPlusIntercept +
numFeatures)
+ margins(i) += localCoefficients(numClasses * numFeatures + i)
}
- if (i == label.toInt) marginOfLabel = margin
- if (margin > maxMargin) {
- maxMargin = margin
+ if (i == label.toInt) marginOfLabel = margins(i)
+ if (margins(i) > maxMargin) {
+ maxMargin = margins(i)
}
- margin
+ i += 1
}
/**
* When maxMargin > 0, the original formula could cause overflow.
* We address this by subtracting maxMargin from all the margins, so
it's guaranteed
* that all of the new margins will be smaller than zero to prevent
arithmetic overflow.
*/
+ val multipliers = new Array[Double](numClasses)
val sum = {
var temp = 0.0
if (maxMargin > 0) {
- for (i <- 0 until numClasses) {
+ var i = 0
--- End diff --
Can this not be:
```scala
val sum = {
var temp = 0.0
var i = 0
while (i < numClasses) {
if (maxMargin > 0) margins(i) -= maxMargin
val exp = math.exp(margins(i))
temp += exp
multipliers(i) = exp
i += 1
}
temp
}
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