Github user iyounus commented on a diff in the pull request:
https://github.com/apache/spark/pull/10274#discussion_r49140607
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala ---
@@ -94,8 +110,7 @@ private[ml] class WeightedLeastSquares(
if (standardizeFeatures) {
lambda *= aVar(j - 2)
}
- if (standardizeLabel) {
- // TODO: handle the case when bStd = 0
+ if (standardizeLabel && bStd != 0) {
--- End diff --
@dbtsai The problem here is that for regularized regression in R, I need to
use `glmnet`. But for this specific case (constant label, no intercept and no
regularization) the results from `glmnet` do no match with `lm`. So I see a
discrepancy within R itself. Have a look at the following R code:
```
A <- matrix(c(0, 1, 2, 3, 5, 7, 11, 13), 4, 2)
b <- c(17, 17, 17, 17)
w <- c(1, 2, 3, 4)
df <- as.data.frame(cbind(A, b))
lm.model <- lm(b ~ . -1, data=df, weights=w)
print(as.vector(coef(lm.model)))
[1] -9.221298 3.394343
glm.model <- glmnet(A, b, weights=w, intercept=FALSE, lambda=0,
standardize=FALSE, alpha=0, thresh=1E-14)
print(as.vector(coef(glm.model)))
[1] 0 0 0
```
Note that in this example, I expect same results from both `lm` and
`glmnet` because I've set `lambda=0` in `glmnet`. (BTW `standardize` has not
effect here.) It seems to me that `glmnet` just sets all coefficients to zero
if label is constant and intercept is not included. This is true even if I
include regularization.
Right now `WeightedLeastSquares` (without regularization) matches with
`lm`, and I think this is the correct behaviour given my understanding of the
normal equation. With regularization, it should still give some non-zero
coefficients, which is does. I don't know why `glmnet` behaves differently, but
I don't think we should try to match that in this particular case.
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