Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/10274#discussion_r49667844
--- 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 --
Here is the exercise,
```scala
test("WLS against lm") {
/*
R code:
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] -3.727121 3.009983
[1] 18.08 6.08 -0.60
*/
val expected = Seq(
Vectors.dense(0.0, -3.727121, 3.009983),
Vectors.dense(18.08, 6.08, -0.60))
var idx = 0
for (fitIntercept <- Seq(false, true)) {
for (standardization <- Seq(false, true)) {
val wls = new WeightedLeastSquares(
fitIntercept, regParam = 0.0, standardizeFeatures =
standardization,
standardizeLabel = standardization).fit(instances)
val actual = Vectors.dense(wls.intercept, wls.coefficients(0),
wls.coefficients(1))
assert(actual ~== expected(idx) absTol 1e-4)
}
idx += 1
}
}
```
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