[GitHub] spark pull request #16699: [SPARK-18710][ML] Add offset in GLM

[sethah <gi...@git.apache.org>]

Github user sethah commented on a diff in the pull request:

    https://github.com/apache/spark/pull/16699#discussion_r99263111
  
    --- 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)
    +          .setFitIntercept(fitIntercept).setOffsetCol("offset")
    +          .setWeightCol("weight").setLinkPredictionCol("linkPrediction")
    +        if (family == "tweedie") trainer = trainer.setVariancePower(1.5)
    +        val model = trainer.fit(dataset)
    +        val actual = Vectors.dense(model.intercept, model.coefficients(0), model.coefficients(1))
    +        assert(actual ~= expected(idx) absTol 1e-4, s"Model mismatch: GLM with family = $family," +
    --- End diff --
    
    We need to be checking more than just the coefficients. For example, the computation of the null deviance does not match R, since the null model computation does not consider the offsets.
    
    Actually, I think we ought to just incorporate offsets into all of the other tests, which will make sure offsets are exhaustively tested. This has been done before e.g. https://github.com/apache/spark/pull/15488, and it _is_ a real pain, but it's probably the best way. I'd be open to other arguments though.


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