Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/7080#discussion_r33532011
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -534,27 +554,39 @@ 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)
--- End diff --
I think it will be even faster than the previous one since I don't have a
copy of wArray anymore, and I compute the gradient and norm in the same pass
while previously, we looped through the array twice. Since it's axpy, the BLAS
will not gain too much performance.
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