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From actuaryzhang <...@git.apache.org>
Subject [GitHub] spark pull request #16699: [SPARK-18710][ML] Add offset in GLM
Date Tue, 27 Jun 2017 21:42:30 GMT
Github user actuaryzhang commented on a diff in the pull request:

    https://github.com/apache/spark/pull/16699#discussion_r124403889
  
    --- Diff: mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala
---
    @@ -798,77 +798,184 @@ class GeneralizedLinearRegressionSuite
         }
       }
     
    -  test("glm summary: gaussian family with weight") {
    +  test("generalized linear regression with offset") {
         /*
    -       R code:
    +      R code:
    +      library(statmod)
     
    -       A <- matrix(c(0, 1, 2, 3, 5, 7, 11, 13), 4, 2)
    -       b <- c(17, 19, 23, 29)
    -       w <- c(1, 2, 3, 4)
    -       df <- as.data.frame(cbind(A, b))
    -     */
    -    val datasetWithWeight = Seq(
    -      Instance(17.0, 1.0, Vectors.dense(0.0, 5.0).toSparse),
    -      Instance(19.0, 2.0, Vectors.dense(1.0, 7.0)),
    -      Instance(23.0, 3.0, Vectors.dense(2.0, 11.0)),
    -      Instance(29.0, 4.0, Vectors.dense(3.0, 13.0))
    +      df <- as.data.frame(matrix(c(
    +        0.2, 1.0, 2.0, 0.0, 5.0,
    +        0.5, 2.1, 0.5, 1.0, 2.0,
    +        0.9, 0.4, 1.0, 2.0, 1.0,
    +        0.7, 0.7, 0.0, 3.0, 3.0), 4, 5, byrow = TRUE))
    +      families <- list(gaussian, binomial, 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]  0.5169222 -0.3344444
    +      [1]  0.9419107 -0.6864404
    +      [1]  0.1812436 -0.6568422
    +      [1] -0.2869094  0.7857710
    +      [1] 0.1055254 0.2979113
    +      [1] -0.05990345  0.53188982 -0.32118415
    +      [1] -0.2147117  0.9911750 -0.6356096
    +      [1] -1.5616130  0.6646470 -0.3192581
    +      [1]  0.3390397 -0.3406099  0.6870259
    +      [1] 0.3665034 0.1039416 0.1484616
    +    */
    +    val dataset = Seq(
    +      OffsetInstance(0.2, 1.0, 2.0, Vectors.dense(0.0, 5.0)),
    +      OffsetInstance(0.5, 2.1, 0.5, Vectors.dense(1.0, 2.0)),
    +      OffsetInstance(0.9, 0.4, 1.0, Vectors.dense(2.0, 1.0)),
    +      OffsetInstance(0.7, 0.7, 0.0, Vectors.dense(3.0, 3.0))
         ).toDF()
    +
    +    val expected = Seq(
    +      Vectors.dense(0, 0.5169222, -0.3344444),
    +      Vectors.dense(0, 0.9419107, -0.6864404),
    +      Vectors.dense(0, 0.1812436, -0.6568422),
    +      Vectors.dense(0, -0.2869094, 0.785771),
    +      Vectors.dense(0, 0.1055254, 0.2979113),
    +      Vectors.dense(-0.05990345, 0.53188982, -0.32118415),
    +      Vectors.dense(-0.2147117, 0.991175, -0.6356096),
    +      Vectors.dense(-1.561613, 0.664647, -0.3192581),
    +      Vectors.dense(0.3390397, -0.3406099, 0.6870259),
    +      Vectors.dense(0.3665034, 0.1039416, 0.1484616))
    +
    +    import GeneralizedLinearRegression._
    +
    +    var idx = 0
    +
    +    for (fitIntercept <- Seq(false, true)) {
    +      for (family <- Seq("gaussian", "binomial", "poisson", "gamma", "tweedie")) {
    +        val trainer = new GeneralizedLinearRegression().setFamily(family)
    +          .setFitIntercept(fitIntercept).setOffsetCol("offset")
    +          .setWeightCol("weight").setLinkPredictionCol("linkPrediction")
    +        if (family == "tweedie") 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," +
    +          s" and fitIntercept = $fitIntercept.")
    +
    +        val familyLink = FamilyAndLink(trainer)
    +        model.transform(dataset).select("features", "offset", "prediction", "linkPrediction")
    +          .collect().foreach {
    +          case Row(features: DenseVector, offset: Double, prediction1: Double,
    +          linkPrediction1: Double) =>
    +            val eta = BLAS.dot(features, model.coefficients) + model.intercept + offset
    +            val prediction2 = familyLink.fitted(eta)
    +            val linkPrediction2 = eta
    +            assert(prediction1 ~= prediction2 relTol 1E-5, "Prediction mismatch: GLM
with " +
    +              s"family = $family, and fitIntercept = $fitIntercept.")
    +            assert(linkPrediction1 ~= linkPrediction2 relTol 1E-5, "Link Prediction mismatch:
" +
    +              s"GLM with family = $family, and fitIntercept = $fitIntercept.")
    +        }
    +
    +        idx += 1
    +      }
    +    }
    +  }
    +
    +  test("generalized linear regression: predict with no offset") {
    +    val trainData = Seq(
    +      OffsetInstance(2.0, 1.0, 2.0, Vectors.dense(0.0, 5.0)),
    +      OffsetInstance(8.0, 2.0, 3.0, Vectors.dense(1.0, 7.0)),
    +      OffsetInstance(3.0, 3.0, 1.0, Vectors.dense(2.0, 11.0)),
    +      OffsetInstance(9.0, 4.0, 4.0, Vectors.dense(3.0, 13.0))
    +    ).toDF()
    +    val testData = trainData.select("weight", "features")
    +
    +    val trainer = new GeneralizedLinearRegression()
    +      .setFamily("poisson")
    +      .setWeightCol("weight")
    +      .setOffsetCol("offset")
    +      .setLinkPredictionCol("linkPrediction")
    +
    +    val model = trainer.fit(trainData)
    +    model.transform(testData).select("features", "linkPrediction")
    +      .collect().foreach {
    +      case Row(features: DenseVector, linkPrediction1: Double) =>
    +        val linkPrediction2 = BLAS.dot(features, model.coefficients) + model.intercept
    +        assert(linkPrediction1 ~= linkPrediction2 relTol 1E-5, "Link Prediction mismatch")
    +    }
    +  }
    +
    +  test("glm summary: gaussian family with weight and offset") {
         /*
    -       R code:
    +      R code:
     
    -       model <- glm(formula = "b ~ .", family="gaussian", data = df, weights = w)
    -       summary(model)
    +      A <- matrix(c(0, 1, 2, 3, 5, 7, 11, 13), 4, 2)
    +      b <- c(17, 19, 23, 29)
    +      w <- c(1, 2, 3, 4)
    +      off <- c(2, 3, 1, 4)
    +      df <- as.data.frame(cbind(A, b))
    +     */
    +    val dataset = Seq(
    +      OffsetInstance(17.0, 1.0, 2.0, Vectors.dense(0.0, 5.0).toSparse),
    +      OffsetInstance(19.0, 2.0, 3.0, Vectors.dense(1.0, 7.0)),
    +      OffsetInstance(23.0, 3.0, 1.0, Vectors.dense(2.0, 11.0)),
    +      OffsetInstance(29.0, 4.0, 4.0, Vectors.dense(3.0, 13.0))
    +    ).toDF()
    +    /*
    +      R code:
     
    -       Deviance Residuals:
    -           1       2       3       4
    -       1.920  -1.358  -1.109   0.960
    +      model <- glm(formula = "b ~ .", family = "gaussian", data = df,
    +                   weights = w, offset = off)
    +      summary(model)
     
    -       Coefficients:
    -                   Estimate Std. Error t value Pr(>|t|)
    -       (Intercept)   18.080      9.608   1.882    0.311
    -       V1             6.080      5.556   1.094    0.471
    -       V2            -0.600      1.960  -0.306    0.811
    +      Deviance Residuals:
    +            1        2        3        4
    +       0.9600  -0.6788  -0.5543   0.4800
     
    -       (Dispersion parameter for gaussian family taken to be 7.68)
    +      Coefficients:
    +                  Estimate Std. Error t value Pr(>|t|)
    +      (Intercept)   5.5400     4.8040   1.153    0.455
    +      V1           -0.9600     2.7782  -0.346    0.788
    +      V2            1.7000     0.9798   1.735    0.333
     
    -           Null deviance: 202.00  on 3  degrees of freedom
    -       Residual deviance:   7.68  on 1  degrees of freedom
    -       AIC: 18.783
    +      (Dispersion parameter for gaussian family taken to be 1.92)
     
    -       Number of Fisher Scoring iterations: 2
    +          Null deviance: 152.10  on 3  degrees of freedom
    +      Residual deviance:   1.92  on 1  degrees of freedom
    +      AIC: 13.238
     
    -       residuals(model, type="pearson")
    -              1         2         3         4
    -       1.920000 -1.357645 -1.108513  0.960000
    +      Number of Fisher Scoring iterations: 2
     
    -       residuals(model, type="working")
    +      residuals(model, type = "pearson")
    +               1          2          3          4
    +      0.9600000 -0.6788225 -0.5542563  0.4800000
    +      residuals(model, type = "working")
               1     2     3     4
    -       1.92 -0.96 -0.64  0.48
    -
    -       residuals(model, type="response")
    +      0.96 -0.48 -0.32  0.24
    --- End diff --
    
    They don't have the same indentation with the old code but within the new block of code,
every line is still aligned. Won't change.


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