[SPARK-19155][ML] MLlib GeneralizedLinearRegression family and link should case insensitive

## What changes were proposed in this pull request?
MLlib ```GeneralizedLinearRegression``` ```family``` and ```link``` should be case insensitive. This is consistent with some other MLlib params such as [```featureSubsetStrategy```](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala#L415).

## How was this patch tested?
Update corresponding tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #16516 from yanboliang/spark-19133.

2 files changed, 6 insertions, 6 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
index a32302bf5d..116f0f6507 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
@@ -57,7 +57,7 @@ private[regression] trait GeneralizedLinearRegressionBase extends PredictorParam
   final val family: Param[String] = new Param(this, "family",
     "The name of family which is a description of the error distribution to be used in the " +
       s"model. Supported options: ${supportedFamilyNames.mkString(", ")}.",
-    ParamValidators.inArray[String](supportedFamilyNames.toArray))
+    (value: String) => supportedFamilyNames.contains(value.toLowerCase))
 
   /** @group getParam */
   @Since("2.0.0")
@@ -74,7 +74,7 @@ private[regression] trait GeneralizedLinearRegressionBase extends PredictorParam
   final val link: Param[String] = new Param(this, "link", "The name of link function " +
     "which provides the relationship between the linear predictor and the mean of the " +
     s"distribution function. Supported options: ${supportedLinkNames.mkString(", ")}",
-    ParamValidators.inArray[String](supportedLinkNames.toArray))
+    (value: String) => supportedLinkNames.contains(value.toLowerCase))
 
   /** @group getParam */
   @Since("2.0.0")
@@ -414,7 +414,7 @@ object GeneralizedLinearRegression extends DefaultParamsReadable[GeneralizedLine
      * @param name family name: "gaussian", "binomial", "poisson" or "gamma".
      */
     def fromName(name: String): Family = {
-      name match {
+      name.toLowerCase match {
         case Gaussian.name => Gaussian
         case Binomial.name => Binomial
         case Poisson.name => Poisson
@@ -626,7 +626,7 @@ object GeneralizedLinearRegression extends DefaultParamsReadable[GeneralizedLine
      *             "inverse", "probit", "cloglog" or "sqrt".
      */
     def fromName(name: String): Link = {
-      name match {
+      name.toLowerCase match {
         case Identity.name => Identity
         case Logit.name => Logit
         case Log.name => Log
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 ed24c1e16a..9f3d643c2b 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
@@ -553,7 +553,7 @@ class GeneralizedLinearRegressionSuite
     for ((link, dataset) <- Seq(("inverse", datasetGammaInverse),
       ("identity", datasetGammaIdentity), ("log", datasetGammaLog))) {
       for (fitIntercept <- Seq(false, true)) {
-        val trainer = new GeneralizedLinearRegression().setFamily("gamma").setLink(link)
+        val trainer = new GeneralizedLinearRegression().setFamily("Gamma").setLink(link)
           .setFitIntercept(fitIntercept).setLinkPredictionCol("linkPrediction")
         val model = trainer.fit(dataset)
         val actual = Vectors.dense(model.intercept, model.coefficients(0), model.coefficients(1))
@@ -990,7 +990,7 @@ class GeneralizedLinearRegressionSuite
        -0.6344390  0.3172195  0.2114797 -0.1586097
      */
     val trainer = new GeneralizedLinearRegression()
-      .setFamily("gamma")
+      .setFamily("Gamma")
       .setWeightCol("weight")
 
     val model = trainer.fit(datasetWithWeight)