Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/15593#discussion_r87547519
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -1486,57 +1504,65 @@ 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) {
- margins(i) -= maxMargin
- temp += math.exp(margins(i))
- }
- } else {
- for (i <- 0 until numClasses) {
- temp += math.exp(margins(i))
- }
+ 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
}
- for (i <- 0 until numClasses) {
- val multiplier = math.exp(margins(i)) / sum - {
- if (label == i) 1.0 else 0.0
- }
- features.foreachActive { (index, value) =>
- if (localFeaturesStd(index) != 0.0 && value != 0.0) {
- localGradientArray(i * numFeaturesPlusIntercept + index) +=
- weight * multiplier * value / localFeaturesStd(index)
+ margins.indices.foreach { i =>
+ multipliers(i) = multipliers(i) / sum - (if (label == i) 1.0 else
0.0)
+ }
+ features.foreachActive { (index, value) =>
+ if (localFeaturesStd(index) != 0.0 && value != 0.0) {
+ val stdValue = value / localFeaturesStd(index)
+ var j = 0
+ while (j < numClasses) {
+ localGradientArray(index * numClasses + j) +=
+ weight * multipliers(j) * stdValue
+ j += 1
}
}
- if (fitIntercept) {
- localGradientArray(i * numFeaturesPlusIntercept + numFeatures) +=
weight * multiplier
+ }
+ if (fitIntercept) {
+ var i = 0
+ while (i < numClasses) {
+ localGradientArray(numFeatures * numClasses + i) += weight *
multipliers(i)
+ i += 1
}
}
--- End diff --
I'm not sure I fully get where you intend to use `foreachActive` over the
gradient matrix? Maybe it's the location of this comment that is confusing me
...
... but here in `multinomialUpdateInPlace`, we are iterating over features
using `foreachActive`, then for each feature iterating over `numClasses`. If we
iterate over the gradient using `foreachActive` how will that work? Won't it be
super inefficient? Perhaps I am missing something about what you intend, could
you clarify with an example?
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