[SPARK-17241][SPARKR][MLLIB] SparkR spark.glm should have configurable regularization parameter

https://issues.apache.org/jira/browse/SPARK-17241

## What changes were proposed in this pull request?

Spark has configurable L2 regularization parameter for generalized linear regression. It is very important to have them in SparkR so that users can run ridge regression.

## How was this patch tested?

Test manually on local laptop.

Author: Xin Ren <iamshrek@126.com>

Closes #14856 from keypointt/SPARK-17241.

2 files changed, 43 insertions, 1 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 0d3181d0ac..7a6ab618a1 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
@@ -69,7 +69,8 @@ private[r] object GeneralizedLinearRegressionWrapper
       link: String,
       tol: Double,
       maxIter: Int,
-      weightCol: String): GeneralizedLinearRegressionWrapper = {
+      weightCol: String,
+      regParam: Double): GeneralizedLinearRegressionWrapper = {
     val rFormula = new RFormula()
       .setFormula(formula)
     val rFormulaModel = rFormula.fit(data)
@@ -86,6 +87,7 @@ private[r] object GeneralizedLinearRegressionWrapper
       .setTol(tol)
       .setMaxIter(maxIter)
       .setWeightCol(weightCol)
+      .setRegParam(regParam)
     val pipeline = new Pipeline()
       .setStages(Array(rFormulaModel, glr))
       .fit(data)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala
index a4568e83fa..d8032c4e17 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala
@@ -1034,6 +1034,46 @@ class GeneralizedLinearRegressionSuite
       .setFamily("gaussian")
       .fit(datasetGaussianIdentity.as[LabeledPoint])
   }
+
+  test("generalized linear regression: regularization parameter") {
+    /*
+      R code:
+
+      a1 <- c(0, 1, 2, 3)
+      a2 <- c(5, 2, 1, 3)
+      b <- c(1, 0, 1, 0)
+      data <- as.data.frame(cbind(a1, a2, b))
+      df <- suppressWarnings(createDataFrame(data))
+
+      for (regParam in c(0.0, 0.1, 1.0)) {
+        model <- spark.glm(df, b ~ a1 + a2, regParam = regParam)
+        print(as.vector(summary(model)$aic))
+      }
+
+      [1] 12.88188
+      [1] 12.92681
+      [1] 13.32836
+     */
+    val dataset = spark.createDataFrame(Seq(
+      LabeledPoint(1, Vectors.dense(5, 0)),
+      LabeledPoint(0, Vectors.dense(2, 1)),
+      LabeledPoint(1, Vectors.dense(1, 2)),
+      LabeledPoint(0, Vectors.dense(3, 3))
+    ))
+    val expected = Seq(12.88188, 12.92681, 13.32836)
+
+    var idx = 0
+    for (regParam <- Seq(0.0, 0.1, 1.0)) {
+      val trainer = new GeneralizedLinearRegression()
+        .setRegParam(regParam)
+        .setLabelCol("label")
+        .setFeaturesCol("features")
+      val model = trainer.fit(dataset)
+      val actual = model.summary.aic
+      assert(actual ~= expected(idx) absTol 1e-4, "Model mismatch: GLM with regParam = $regParam.")
+      idx += 1
+    }
+  }
 }
 
 object GeneralizedLinearRegressionSuite {