fixed nonserializable error that was causing naivebayes test failures

Author: leahmcguire

mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala

@@ -46,19 +46,19 @@ class NaiveBayesModel private[mllib] (
     val labels: Array[Double],
     val pi: Array[Double],
     val theta: Array[Array[Double]],
-    val modelType: NaiveBayes.ModelType)
+    val modelType: String)
   extends ClassificationModel with Serializable with Saveable {
 
   def this(labels: Array[Double], pi: Array[Double], theta: Array[Array[Double]]) =
-    this(labels, pi, theta, NaiveBayes.Multinomial)
+    this(labels, pi, theta, NaiveBayes.Multinomial.toString)
 
   private val brzPi = new BDV[Double](pi)
   private val brzTheta = new BDM(theta(0).length, theta.length, theta.flatten).t
 
   // Bernoulli scoring requires log(condprob) if 1 log(1-condprob) if 0
   // this precomputes log(1.0 - exp(theta)) and its sum for linear algebra application
   // of this condition in predict function
-  private val (brzNegTheta, brzNegThetaSum) = modelType match {
+  private val (brzNegTheta, brzNegThetaSum) = NaiveBayes.ModelType.fromString(modelType) match {
     case NaiveBayes.Multinomial => (None, None)
     case NaiveBayes.Bernoulli =>
       val negTheta = brzLog((brzExp(brzTheta.copy) :*= (-1.0)) :+= 1.0) // log(1.0 - exp(x))
@@ -74,7 +74,7 @@ class NaiveBayesModel private[mllib] (
   }
 
   override def predict(testData: Vector): Double = {
-    modelType match {
+    NaiveBayes.ModelType.fromString(modelType) match {
       case NaiveBayes.Multinomial =>
         labels (brzArgmax (brzPi + brzTheta * testData.toBreeze) )
       case NaiveBayes.Bernoulli =>
@@ -84,7 +84,7 @@ class NaiveBayesModel private[mllib] (
   }
 
   override def save(sc: SparkContext, path: String): Unit = {
-    val data = NaiveBayesModel.SaveLoadV1_0.Data(labels, pi, theta, modelType.toString)
+    val data = NaiveBayesModel.SaveLoadV1_0.Data(labels, pi, theta, modelType)
     NaiveBayesModel.SaveLoadV1_0.save(sc, path, data)
   }
 
@@ -137,15 +137,15 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] {
       val labels = data.getAs[Seq[Double]](0).toArray
       val pi = data.getAs[Seq[Double]](1).toArray
       val theta = data.getAs[Seq[Seq[Double]]](2).map(_.toArray).toArray
-      val modelType = NaiveBayes.ModelType.fromString(data.getString(3))
+      val modelType = NaiveBayes.ModelType.fromString(data.getString(3)).toString
       new NaiveBayesModel(labels, pi, theta, modelType)
     }
   }
 
   override def load(sc: SparkContext, path: String): NaiveBayesModel = {
-    def getModelType(metadata: JValue): NaiveBayes.ModelType = {
+    def getModelType(metadata: JValue): String = {
       implicit val formats = DefaultFormats
-      NaiveBayes.ModelType.fromString((metadata \ "modelType").extract[String])
+      NaiveBayes.ModelType.fromString((metadata \ "modelType").extract[String]).toString
     }
     val (loadedClassName, version, metadata) = loadMetadata(sc, path)
     val classNameV1_0 = SaveLoadV1_0.thisClassName
@@ -265,7 +265,7 @@ class NaiveBayes private (
       i += 1
     }
 
-    new NaiveBayesModel(labels, pi, theta, modelType)
+    new NaiveBayesModel(labels, pi, theta, modelType.toString)
   }
 }
 

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

@@ -52,7 +52,7 @@ object NaiveBayesSuite {
     sample: Int = 10): Seq[LabeledPoint] = {
     val D = theta(0).length
     val rnd = new Random(seed)
-
+c
     val _pi = pi.map(math.pow(math.E, _))
     val _theta = theta.map(row => row.map(math.pow(math.E, _)))
 
@@ -77,7 +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)), NaiveBayes.Bernoulli)
+    theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)), NaiveBayes.Bernoulli.toString)
 }
 
 class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
@@ -111,7 +111,6 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
 
   test("Naive Bayes Multinomial") {
     val nPoints = 1000
-
     val pi = Array(0.5, 0.1, 0.4).map(math.log)
     val theta = Array(
       Array(0.70, 0.10, 0.10, 0.10), // label 0
@@ -120,7 +119,11 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
     ).map(_.map(math.log))
 
     val testData = NaiveBayesSuite.generateNaiveBayesInput(
-      pi, theta, nPoints, 42, NaiveBayes.Multinomial)
+      pi,
+      theta,
+      nPoints,
+      42,
+      NaiveBayes.Multinomial)
     val testRDD = sc.parallelize(testData, 2)
     testRDD.cache()
 
@@ -144,7 +147,6 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
 
   test("Naive Bayes Bernoulli") {
     val nPoints = 10000
-
     val pi = Array(0.5, 0.3, 0.2).map(math.log)
     val theta = Array(
       Array(0.50, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.40), // label 0