Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/10274#discussion_r51059677
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala
---
@@ -74,6 +89,35 @@ class WeightedLeastSquaresSuite extends SparkFunSuite
with MLlibTestSparkContext
}
}
+ test("WLS against lm when label is constant") {
+ /*
+ R code:
+ # here b is constant
+ df <- as.data.frame(cbind(A, b))
+ for (formula in c(b ~ . -1, b ~ .)) {
+ model <- lm(formula, data=df, weights=w)
+ print(as.vector(coef(model)))
+ }
+
+ [1] -9.221298 3.394343
+ [1] 17 0 0
+ */
+
+ val expected = Seq(
+ Vectors.dense(0.0, -9.221298, 3.394343),
+ Vectors.dense(17.0, 0.0, 0.0))
+
+ var idx = 0
+ for (fitIntercept <- Seq(false, true)) {
+ val wls = new WeightedLeastSquares(
+ fitIntercept, regParam = 0.0, standardizeFeatures = false,
standardizeLabel = true)
+ .fit(instancesConstLabel)
--- End diff --
Sorry for getting you back so late. The difference is due to that `glmnet`
always standardizes labels even `standardization == false`. `standardization ==
false` is turning off the standardization on features. As a result, at least in
`glmnet`, when `ystd == 0.0`, the training is not valid.
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