updated test suite with model type fix

Author: leahmcguire

mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala

@@ -25,7 +25,6 @@ import scala.util.Random
 import org.scalatest.FunSuite
 
 import org.apache.spark.SparkException
-import org.apache.spark.mllib.classification.NaiveBayesModels.NaiveBayesModels
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext}
@@ -49,7 +48,7 @@ object NaiveBayesSuite {
     theta: Array[Array[Double]],  // CXD
     nPoints: Int,
     seed: Int,
-    dataModel: NaiveBayesModels= NaiveBayesModels.Multinomial,
+    dataModel: NaiveBayes.ModelType = NaiveBayes.Multinomial,
     sample: Int = 10): Seq[LabeledPoint] = {
     val D = theta(0).length
     val rnd = new Random(seed)
@@ -60,10 +59,10 @@ object NaiveBayesSuite {
     for (i <- 0 until nPoints) yield {
       val y = calcLabel(rnd.nextDouble(), _pi)
       val xi = dataModel match {
-        case NaiveBayesModels.Bernoulli => Array.tabulate[Double] (D) {j =>
+        case NaiveBayes.Bernoulli => Array.tabulate[Double] (D) {j =>
             if (rnd.nextDouble () < _theta(y)(j) ) 1 else 0
         }
-        case NaiveBayesModels.Multinomial =>
+        case NaiveBayes.Multinomial =>
           val mult = Multinomial(BDV(_theta(y)))
           val emptyMap = (0 until D).map(x => (x, 0.0)).toMap
           val counts = emptyMap ++ mult.sample(sample).groupBy(x => x).map {
@@ -78,7 +77,7 @@ object NaiveBayesSuite {
 
   /** Binary labels, 3 features */
   private val binaryModel = new NaiveBayesModel(labels = Array(0.0, 1.0), pi = Array(0.2, 0.8),
-    theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)), NaiveBayesModels.Bernoulli)
+    theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)), NaiveBayes.Bernoulli)
 }
 
 class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
@@ -121,7 +120,7 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
     ).map(_.map(math.log))
 
     val testData = NaiveBayesSuite.generateNaiveBayesInput(
-      pi, theta, nPoints, 42, NaiveBayesModels.Multinomial)
+      pi, theta, nPoints, 42, NaiveBayes.Multinomial)
     val testRDD = sc.parallelize(testData, 2)
     testRDD.cache()
 
@@ -133,7 +132,7 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
       theta,
       nPoints,
       17,
-      NaiveBayesModels.Multinomial)
+      NaiveBayes.Multinomial)
     val validationRDD = sc.parallelize(validationData, 2)
 
     // Test prediction on RDD.
@@ -158,7 +157,7 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
       theta,
       nPoints,
       45,
-      NaiveBayesModels.Bernoulli)
+      NaiveBayes.Bernoulli)
     val testRDD = sc.parallelize(testData, 2)
     testRDD.cache()
 
@@ -170,7 +169,7 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
       theta,
       nPoints,
       20,
-      NaiveBayesModels.Bernoulli)
+      NaiveBayes.Bernoulli)
     val validationRDD = sc.parallelize(validationData, 2)
 
     // Test prediction on RDD.