[SPARK-19391][SPARKR][ML] Tweedie GLM API for SparkR

## What changes were proposed in this pull request?
Port Tweedie GLM  #16344  to SparkR

felixcheung yanboliang

## How was this patch tested?
new test in SparkR

Author: actuaryzhang <actuaryzhang10@gmail.com>

Closes #16729 from actuaryzhang/sparkRTweedie.

1 files changed, 15 insertions, 4 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala
index cbd6cd1c79..c49416b240 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala
@@ -71,7 +71,9 @@ private[r] object GeneralizedLinearRegressionWrapper
       tol: Double,
       maxIter: Int,
       weightCol: String,
-      regParam: Double): GeneralizedLinearRegressionWrapper = {
+      regParam: Double,
+      variancePower: Double,
+      linkPower: Double): GeneralizedLinearRegressionWrapper = {
     val rFormula = new RFormula().setFormula(formula)
     checkDataColumns(rFormula, data)
     val rFormulaModel = rFormula.fit(data)
@@ -83,13 +85,17 @@ private[r] object GeneralizedLinearRegressionWrapper
     // assemble and fit the pipeline
     val glr = new GeneralizedLinearRegression()
       .setFamily(family)
-      .setLink(link)
       .setFitIntercept(rFormula.hasIntercept)
       .setTol(tol)
       .setMaxIter(maxIter)
       .setRegParam(regParam)
       .setFeaturesCol(rFormula.getFeaturesCol)
-
+    // set variancePower and linkPower if family is tweedie; otherwise, set link function
+    if (family.toLowerCase == "tweedie") {
+      glr.setVariancePower(variancePower).setLinkPower(linkPower)
+    } else {
+      glr.setLink(link)
+    }
     if (weightCol != null) glr.setWeightCol(weightCol)
 
     val pipeline = new Pipeline()
@@ -145,7 +151,12 @@ private[r] object GeneralizedLinearRegressionWrapper
     val rDeviance: Double = summary.deviance
     val rResidualDegreeOfFreedomNull: Long = summary.residualDegreeOfFreedomNull
     val rResidualDegreeOfFreedom: Long = summary.residualDegreeOfFreedom
-    val rAic: Double = summary.aic
+    val rAic: Double = if (family.toLowerCase == "tweedie" &&
+      !Array(0.0, 1.0, 2.0).exists(x => math.abs(x - variancePower) < 1e-8)) {
+      0.0
+    } else {
+      summary.aic
+    }
     val rNumIterations: Int = summary.numIterations
 
     new GeneralizedLinearRegressionWrapper(pipeline, rFeatures, rCoefficients, rDispersion,