Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/16699#discussion_r98300999
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala
---
@@ -743,6 +743,84 @@ class GeneralizedLinearRegressionSuite
}
}
+ test("generalized linear regression with offset") {
+ /*
+ R code:
+ library(statmod)
+ df <- as.data.frame(matrix(c(
+ 1.0, 1.0, 2.0, 0.0, 5.0,
+ 2.0, 2.0, 0.5, 1.0, 2.0,
+ 1.0, 3.0, 1.0, 2.0, 1.0,
+ 2.0, 4.0, 0.0, 3.0, 3.0), 4, 5, byrow = TRUE))
+ families <- list(gaussian, poisson, Gamma, tweedie(1.5))
+ f1 <- V1 ~ -1 + V4 + V5
+ f2 <- V1 ~ V4 + V5
+ for (f in c(f1, f2)) {
+ for (fam in families) {
+ model <- glm(f, df, family = fam, weights = V2, offset = V3)
+ print(as.vector(coef(model)))
+ }
+ }
+
+ [1] 0.535040431 0.005390836
+ [1] 0.1968355 -0.2061711
+ [1] 0.307996 -0.153579
+ [1] 0.32166185 -0.09698986
+ [1] -0.8800000 0.7342857 0.1714286
+ [1] -1.9991044 0.7247511 0.1424392
+ [1] -0.27378146 0.31599396 -0.06204946
+ [1] -0.17118812 0.31200361 -0.02541656
+ */
+ val dataset = Seq(
+ OffsetInstance(1.0, 1.0, 2.0, Vectors.dense(0.0, 5.0)),
+ OffsetInstance(2.0, 2.0, 0.5, Vectors.dense(1.0, 2.0)),
+ OffsetInstance(1.0, 3.0, 1.0, Vectors.dense(2.0, 1.0)),
+ OffsetInstance(2.0, 4.0, 0.0, Vectors.dense(3.0, 3.0))
+ ).toDF()
+
+ val expected = Seq(
+ Vectors.dense(0.0, 0.535040431, 0.005390836),
+ Vectors.dense(0.0, 0.1968355, -0.2061711),
+ Vectors.dense(0.0, 0.307996, -0.153579),
+ Vectors.dense(0.0, 0.32166185, -0.09698986),
+ Vectors.dense(-0.88, 0.7342857, 0.1714286),
+ Vectors.dense(-1.9991044, 0.7247511, 0.1424392),
+ Vectors.dense(-0.27378146, 0.31599396, -0.06204946),
+ Vectors.dense(-0.17118812, 0.31200361, -0.02541656))
+
+ import GeneralizedLinearRegression._
+
+ var idx = 0
+ for (fitIntercept <- Seq(false, true)) {
+ for (family <- Seq("gaussian", "poisson", "gamma", "tweedie")) {
+ var trainer = new GeneralizedLinearRegression().setFamily(family)
--- End diff --
just a suggestion: maybe refactor the code below in a method and do
fitIntercept.map(fi => family.map(fm => (fi, fm)).zip(expected).foreach(params
=> callMyMethod(params._1, params._2, params._3))
then you would get rid of the for loops and not have one long test case and
you could remove the idx += 1 below
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