Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/15593#discussion_r87550060
--- 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 --
My (perhaps incorrect) understanding of what you are saying:
"The aggregator should internally convert to col-major during training &
in-place update and from `gradient` will return a Matrix that can be used
during L2 update (cycle through using `foreachActive`) in L1678-L1716."
But to me that would require swapping `coeffs.foreachActive` for
`gradient.foreachActive` in L1684. Since `coeff` is itself a vector (which we
can't really change due to `BreezeDiffFunction`, and we laid it out col-major
for training) we would need to index back into that using the same col-major
indexing anyway, no? So I don't see that we gain anything for L2 reg. Also, if
we change back and forth every iteration that seems like it would be
inefficient.
For the L1 reg function, that is purely index-based so `val isIntercept =
$(fitIntercept) && ((index + 1) % numFeaturesPlusIntercept == 0)` doesn't seem
any more or less complex than `val isIntercept = $(fitIntercept) && index >=
numFeatures * numCoefficientSets`. So I don't think we gain anything during L1
reg function either.
Finally, during initial coefficients setup, we already loop through them if
they are provided. If not, the intercept adjustments are the only things that
occur (the rest are zeros) so that is just indexing into the intercepts, which
whether row- or col-major doesn't seem to me to be a major issue.
I tend to agree that the col-major indexing is no "more complex" than
row-major. It's also commented fairly clearly why and where we lay it out
col-major (with example of the form), and where we convert back to row-major.
We can't also need to convert back to row-major outside of the aggregator
because the coefficients are returned from `optimizer.states.x` which is
Breeze-specific, not `LogisticAggregator` or even `LogisticCostFun`.
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