Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7080#discussion_r33739057
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -534,27 +556,41 @@ private class LogisticCostFun(
case (aggregator1, aggregator2) => aggregator1.merge(aggregator2)
})
- // regVal is the sum of weight squares for L2 regularization
- val norm = if (regParamL2 == 0.0) {
- 0.0
- } else if (fitIntercept) {
- brzNorm(Vectors.dense(weights.toArray.slice(0, weights.size
-1)).toBreeze, 2.0)
- } else {
- brzNorm(weights, 2.0)
- }
- val regVal = 0.5 * regParamL2 * norm * norm
+ val totalGradientArray = logisticAggregator.gradient.toArray
- val loss = logisticAggregator.loss + regVal
- val gradient = logisticAggregator.gradient
-
- if (fitIntercept) {
- val wArray = w.toArray.clone()
- wArray(wArray.length - 1) = 0.0
- axpy(regParamL2, Vectors.dense(wArray), gradient)
+ // regVal is the sum of weight squares excluding intercept for L2
regularization.
+ val regVal = if (regParamL2 == 0.0) {
+ 0.0
} else {
- axpy(regParamL2, w, gradient)
+ var sum = 0.0
+ w.foreachActive { (index, value) =>
+ // If `fitIntercept` is true, the last term which is intercept
doesn't
+ // contribute to the regularization.
+ if (index != numFeatures) {
+ // The following code will compute the loss of the
regularization; also
+ // the gradient of the regularization, and add back to
totalGradientArray.
+ sum += {
+ if (standardization) {
+ totalGradientArray(index) += regParamL2 * value
+ value * value
+ } else {
+ if (featuresStd(index) != 0.0) {
+ // If `standardization` is false, we still standardize the
data
+ // to improve the rate of convergence; as a result, we
have to
+ // perform this reverse standardization by penalizing each
component
+ // differently to get effectively the same objective
function when
+ // the training dataset is not standardized.
--- End diff --
We should check R's implementation and discuss whether this is what users
expect if they set `standardization` to false.
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