add case _ back

apache · May 20, 2015 · 264a814 · 264a814
1 parent 3c456a8
commit 264a814
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Showing 2 changed files with 14 additions and 4 deletions.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -62,7 +62,8 @@ class NaiveBayesModel private[mllib] (
       theta: JIterable[JIterable[Double]]) =
     this(labels.asScala.toArray, pi.asScala.toArray, theta.asScala.toArray.map(_.asScala.toArray))
 
-  require(supportedModelTypes.contains(modelType), s"Invalid model type $modelType.")
+  require(supportedModelTypes.contains(modelType),
+    s"Invalid model type $modelType. Supported model types are $supportedModelTypes.")
 
   // Bernoulli scoring requires log(condprob) if 1, log(1-condprob) if 0.
   // This precomputes log(1.0 - exp(theta)) and its sum which are used for the linear algebra
@@ -76,6 +77,9 @@ class NaiveBayesModel private[mllib] (
         value - math.log(1.0 - math.exp(value))
       }
       (Option(thetaMinusNegTheta), Option(negTheta.multiply(ones)))
+    case _ =>
+      // This should never happen.
+      throw new UnknownError(s"Invalid model type: $modelType.")
   }
 
   override def predict(testData: RDD[Vector]): RDD[Double] = {
@@ -103,6 +107,9 @@ class NaiveBayesModel private[mllib] (
         BLAS.axpy(1.0, piVector, prob)
         BLAS.axpy(1.0, negThetaSum.get, prob)
         labels(prob.argmax)
+      case _ =>
+        // This should never happen.
+        throw new UnknownError(s"Invalid model type: $modelType.")
     }
   }
 
@@ -275,7 +282,7 @@ class NaiveBayes private (
    */
   def setModelType(modelType: String): NaiveBayes = {
     require(NaiveBayes.supportedModelTypes.contains(modelType),
-      s"NaiveBayes was created with an unknown ModelType: $modelType.")
+      s"NaiveBayes was created with an unknown model type: $modelType.")
     this.modelType = modelType
     this
   }
@@ -352,6 +359,9 @@ class NaiveBayes private (
       val thetaLogDenom = modelType match {
         case Multinomial => math.log(sumTermFreqs.values.sum + numFeatures * lambda)
         case Bernoulli => math.log(n + 2.0 * lambda)
+        case _ =>
+          // This should never happen.
+          throw new UnknownError(s"Invalid model type: $modelType.")
       }
       var j = 0
       while (j < numFeatures) {
@@ -430,7 +440,7 @@ object NaiveBayes {
    */
   def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel = {
     require(supportedModelTypes.contains(modelType),
-      s"NaiveBayes was created with an unknown ModelType: $modelType")
+      s"NaiveBayes was created with an unknown model type: $modelType.")
     new NaiveBayes(lambda, modelType).run(input)
   }
 

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
@@ -144,7 +144,7 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
     validateModelFit(pi, theta, model)
 
     val validationData = NaiveBayesSuite.generateNaiveBayesInput(
-      pi, theta, nPoints, 17, Bernoulli)
+      pi, theta, nPoints, 17, Multinomial)
     val validationRDD = sc.parallelize(validationData, 2)
 
     // Test prediction on RDD.