Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/13796#discussion_r75230308
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -982,45 +1275,13 @@ private class LogisticAggregator(
"coefficients only supports dense vector" +
s"but got type ${bcCoefficients.value.getClass}.")
}
- val localGradientSumArray = gradientSumArray
-
- val featuresStd = bcFeaturesStd.value
- numClasses match {
- case 2 =>
- // For Binary Logistic Regression.
- val margin = - {
- var sum = 0.0
- features.foreachActive { (index, value) =>
- if (featuresStd(index) != 0.0 && value != 0.0) {
- sum += coefficientsArray(index) * (value /
featuresStd(index))
- }
- }
- sum + {
- if (fitIntercept) coefficientsArray(numFeatures) else 0.0
- }
- }
-
- val multiplier = weight * (1.0 / (1.0 + math.exp(margin)) -
label)
-
- features.foreachActive { (index, value) =>
- if (featuresStd(index) != 0.0 && value != 0.0) {
- localGradientSumArray(index) += multiplier * (value /
featuresStd(index))
- }
- }
-
- if (fitIntercept) {
- localGradientSumArray(numFeatures) += multiplier
- }
- if (label > 0) {
- // The following is equivalent to log(1 + exp(margin)) but
more numerically stable.
- lossSum += weight * MLUtils.log1pExp(margin)
- } else {
- lossSum += weight * (MLUtils.log1pExp(margin) - margin)
- }
- case _ =>
- new NotImplementedError("LogisticRegression with ElasticNet in
ML package " +
- "only supports binary classification for now.")
+ if (multinomial) {
+ multinomialUpdateInPlace(features, weight, label,
coefficientsArray, gradientSumArray,
+ bcFeaturesStd.value, numFeaturesPlusIntercept)
--- End diff --
I removed the class variables from the functions. For the performance
critical arrays, I make local copies to inside the functions.
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