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 } } ```
--- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org