[SPARK-21275][ML] Update GLM test to use supportedFamilyNames

## What changes were proposed in this pull request?
Update GLM test to use supportedFamilyNames as suggested here:
#16699 (diff)

Author: actuaryzhang <actuaryzhang10@gmail.com>

Closes #18495 from actuaryzhang/mlGlmTest2.

mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala

@@ -749,15 +749,15 @@ class GeneralizedLinearRegressionSuite
       library(statmod)
       y <- c(1.0, 0.5, 0.7, 0.3)
       w <- c(1, 2, 3, 4)
-      for (fam in list(gaussian(), poisson(), binomial(), Gamma(), tweedie(1.6))) {
+      for (fam in list(binomial(), Gamma(), gaussian(), poisson(), tweedie(1.6))) {
         model1 <- glm(y ~ 1, family = fam)
         model2 <- glm(y ~ 1, family = fam, weights = w)
         print(as.vector(c(coef(model1), coef(model2))))
       }
-      [1] 0.625 0.530
-      [1] -0.4700036 -0.6348783
       [1] 0.5108256 0.1201443
       [1] 1.600000 1.886792
+      [1] 0.625 0.530
+      [1] -0.4700036 -0.6348783
       [1] 1.325782 1.463641
      */
 
@@ -768,13 +768,13 @@ class GeneralizedLinearRegressionSuite
       Instance(0.3, 4.0, Vectors.zeros(0))
     ).toDF()
 
-    val expected = Seq(0.625, 0.530, -0.4700036, -0.6348783, 0.5108256, 0.1201443,
-      1.600000, 1.886792, 1.325782, 1.463641)
+    val expected = Seq(0.5108256, 0.1201443, 1.600000, 1.886792, 0.625, 0.530,
+      -0.4700036, -0.6348783, 1.325782, 1.463641)
 
     import GeneralizedLinearRegression._
 
     var idx = 0
-    for (family <- Seq("gaussian", "poisson", "binomial", "gamma", "tweedie")) {
+    for (family <- GeneralizedLinearRegression.supportedFamilyNames.sortWith(_ < _)) {
       for (useWeight <- Seq(false, true)) {
         val trainer = new GeneralizedLinearRegression().setFamily(family)
         if (useWeight) trainer.setWeightCol("weight")
@@ -807,7 +807,7 @@ class GeneralizedLinearRegressionSuite
         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))
+      families <- list(binomial, Gamma, gaussian, poisson, tweedie(1.5))
       f1 <- V1 ~ -1 + V4 + V5
       f2 <- V1 ~ V4 + V5
       for (f in c(f1, f2)) {
@@ -816,15 +816,15 @@ class GeneralizedLinearRegressionSuite
           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]  0.5169222 -0.3344444
+      [1]  0.1812436 -0.6568422
       [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] -0.05990345  0.53188982 -0.32118415
+      [1] -1.5616130  0.6646470 -0.3192581
       [1] 0.3665034 0.1039416 0.1484616
     */
     val dataset = Seq(
@@ -835,23 +835,22 @@ class GeneralizedLinearRegressionSuite
     ).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, 0.5169222, -0.3344444),
+      Vectors.dense(0, 0.1812436, -0.6568422),
       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(-0.05990345, 0.53188982, -0.32118415),
+      Vectors.dense(-1.561613, 0.664647, -0.3192581),
       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")) {
+      for (family <- GeneralizedLinearRegression.supportedFamilyNames.sortWith(_ < _)) {
         val trainer = new GeneralizedLinearRegression().setFamily(family)
           .setFitIntercept(fitIntercept).setOffsetCol("offset")
           .setWeightCol("weight").setLinkPredictionCol("linkPrediction")