renaming functions

Author: manishamde

mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala

@@ -547,16 +547,18 @@ object DecisionTree extends Serializable with Logging {
 
       /**
        * Sequential search helper method to find bin for categorical feature in multiclass
-       * classification. Dummy value of 0 used since it is not used in future calculation
+       * classification. The category is returned since each category can belong to multiple
+       * splits. The actual left/right child allocation per split is performed in the
+       * sequential phase of the bin aggregate operation.
        */
-      def sequentialBinSearchForCategoricalFeatureInBinaryClassification(): Int = {
+      def sequentialBinSearchForCategoricalFeatureInMulticlassClassification(): Int = {
         labeledPoint.features(featureIndex).toInt
       }
 
       /**
        * Sequential search helper method to find bin for categorical feature.
        */
-      def sequentialBinSearchForCategoricalFeatureInMultiClassClassification(): Int = {
+      def sequentialBinSearchForCategoricalFeatureInBinaryClassification(): Int = {
         val featureCategories = strategy.categoricalFeaturesInfo(featureIndex)
         val numCategoricalBins = math.pow(2.0, featureCategories - 1).toInt - 1
         var binIndex = 0
@@ -583,9 +585,9 @@ object DecisionTree extends Serializable with Logging {
         // Perform sequential search to find bin for categorical features.
         val binIndex = {
           if (isMulticlassClassification) {
-            sequentialBinSearchForCategoricalFeatureInBinaryClassification()
+            sequentialBinSearchForCategoricalFeatureInMulticlassClassification()
           } else {
-            sequentialBinSearchForCategoricalFeatureInMultiClassClassification()
+            sequentialBinSearchForCategoricalFeatureInBinaryClassification()
           }
         }
         if (binIndex == -1){
@@ -684,7 +686,7 @@ object DecisionTree extends Serializable with Logging {
      * @return Array[Double] storing aggregate calculation of size
      *         2 * numSplits * numFeatures * numNodes for classification
      */
-    def binaryClassificationBinSeqOp(arr: Array[Double], agg: Array[Double]) = {
+    def orderedClassificationBinSeqOp(arr: Array[Double], agg: Array[Double]) = {
       // Iterate over all nodes.
       var nodeIndex = 0
       while (nodeIndex < numNodes) {
@@ -716,7 +718,7 @@ object DecisionTree extends Serializable with Logging {
      * @return Array[Double] storing aggregate calculation of size
      *         2 * numClasses * numSplits * numFeatures * numNodes for classification
      */
-    def multiClassificationBinSeqOp(arr: Array[Double], agg: Array[Double]) = {
+    def unorderedClassificationBinSeqOp(arr: Array[Double], agg: Array[Double]) = {
       // Iterate over all nodes.
       var nodeIndex = 0
       while (nodeIndex < numNodes) {
@@ -789,9 +791,9 @@ object DecisionTree extends Serializable with Logging {
       strategy.algo match {
         case Classification =>
           if(isMulticlassClassificationWithCategoricalFeatures) {
-            multiClassificationBinSeqOp(arr, agg)
+            unorderedClassificationBinSeqOp(arr, agg)
           } else {
-            binaryClassificationBinSeqOp(arr, agg)
+            orderedClassificationBinSeqOp(arr, agg)
           }
         case Regression => regressionBinSeqOp(arr, agg)
       }