code cleanup

Author: manishamde

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

@@ -144,6 +144,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     new DecisionTreeModel(topNode, strategy.algo)
   }
 
+  // TODO: Unit test this
   /**
    * Extract the decision tree node information for the given tree level and node index
    */
@@ -161,6 +162,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     nodes(nodeIndex) = node
   }
 
+  // TODO: Unit test this
   /**
    *  Extract the decision tree node information for the children of the node
    */
@@ -458,6 +460,8 @@ object DecisionTree extends Serializable with Logging {
     logDebug("numClasses = " + numClasses)
     val labelWeights = strategy.labelWeights
     logDebug("labelWeights = " + labelWeights)
+    val isMulticlassClassification = strategy.isMulticlassClassification
+    logDebug("isMulticlassClassification = " + isMulticlassClassification)
 
 
     // shift when more than one group is used at deep tree level
@@ -582,7 +586,7 @@ object DecisionTree extends Serializable with Logging {
       } else {
         // Perform sequential search to find bin for categorical features.
         val binIndex = {
-          if (strategy.isMultiClassification) {
+          if (isMulticlassClassification) {
             sequentialBinSearchForCategoricalFeatureInBinaryClassification()
           } else {
             sequentialBinSearchForCategoricalFeatureInMultiClassClassification()
@@ -606,7 +610,9 @@ object DecisionTree extends Serializable with Logging {
     def findBinsForLevel(labeledPoint: WeightedLabeledPoint): Array[Double] = {
       // Calculate bin index and label per feature per node.
       val arr = new Array[Double](1 + (numFeatures * numNodes))
+      // First element of the array is the label of the instance.
       arr(0) = labeledPoint.label
+      // Iterate over nodes.
       var nodeIndex = 0
       while (nodeIndex < numNodes) {
         val parentFilters = findParentFilters(nodeIndex)
@@ -629,7 +635,10 @@ object DecisionTree extends Serializable with Logging {
       arr
     }
 
-    /**
+     // Find feature bins for all nodes at a level.
+     val binMappedRDD = input.map(x => findBinsForLevel(x))
+
+      /**
      * Performs a sequential aggregation over a partition for classification. For l nodes,
      * k features, either the left count or the right count of one of the p bins is
      * incremented based upon whether the feature is classified as 0 or 1.
@@ -663,7 +672,7 @@ object DecisionTree extends Serializable with Logging {
             label.toInt match {
               case n: Int =>
                 val isFeatureContinuous = strategy.categoricalFeaturesInfo.get(featureIndex).isEmpty
-                if (!isFeatureContinuous && strategy.isMultiClassification) {
+                if (!isFeatureContinuous && isMulticlassClassification) {
                   // Find all matching bins and increment their values
                   val featureCategories = strategy.categoricalFeaturesInfo(featureIndex)
                   val numCategoricalBins = math.pow(2.0, featureCategories - 1).toInt - 1
@@ -736,7 +745,6 @@ object DecisionTree extends Serializable with Logging {
       agg
     }
 
-    // TODO: Double-check this
     // Calculate bin aggregate length for classification or regression.
     val binAggregateLength = strategy.algo match {
       case Classification => numClasses * numBins * numFeatures * numNodes
@@ -760,9 +768,6 @@ object DecisionTree extends Serializable with Logging {
       combinedAggregate
     }
 
-    // Find feature bins for all nodes at a level.
-    val binMappedRDD = input.map(x => findBinsForLevel(x))
-
     // Calculate bin aggregates.
     val binAggregates = {
       binMappedRDD.aggregate(Array.fill[Double](binAggregateLength)(0))(binSeqOp,binCombOp)
@@ -922,7 +927,7 @@ object DecisionTree extends Serializable with Logging {
           val leftNodeAgg = Array.ofDim[Double](numFeatures, numBins - 1, numClasses)
           val rightNodeAgg = Array.ofDim[Double](numFeatures, numBins - 1, numClasses)
 
-          if (strategy.isMultiClassification) {
+          if (isMulticlassClassification) {
             var featureIndex = 0
             while (featureIndex < numFeatures){
               var splitIndex = 0
@@ -1096,7 +1101,7 @@ object DecisionTree extends Serializable with Logging {
               numBins - 1
             } else { // Categorical feature
               val featureCategories = strategy.categoricalFeaturesInfo(featureIndex)
-              if (strategy.isMultiClassification) {
+              if (isMulticlassClassification) {
                 math.pow(2.0, featureCategories - 1).toInt - 1
               } else { // Binary classification
                 featureCategories
@@ -1177,6 +1182,9 @@ object DecisionTree extends Serializable with Logging {
     val maxBins = strategy.maxBins
     val numBins = if (maxBins <= count) maxBins else count.toInt
     logDebug("numBins = " + numBins)
+    val isMulticlassClassification = strategy.isMulticlassClassification
+    logDebug("isMulticlassClassification = " + isMulticlassClassification)
+
 
     /*
      * Ensure #bins is always greater than the categories. For multiclass classification,
@@ -1187,7 +1195,7 @@ object DecisionTree extends Serializable with Logging {
     if (strategy.categoricalFeaturesInfo.size > 0) {
       val maxCategoriesForFeatures = strategy.categoricalFeaturesInfo.maxBy(_._2)._2
       require(numBins > maxCategoriesForFeatures)
-      if (strategy.isMultiClassification) {
+      if (isMulticlassClassification) {
         require(numBins > math.pow(2, maxCategoriesForFeatures.toInt - 1) - 1)
       }
     }
@@ -1230,7 +1238,7 @@ object DecisionTree extends Serializable with Logging {
             val featureCategories = strategy.categoricalFeaturesInfo(featureIndex)
 
             // Use different bin/split calculation strategy for multiclass classification
-            if (strategy.isMultiClassification) {
+            if (isMulticlassClassification) {
               // 2^(maxFeatureValue- 1) - 1 combinations
               var index = 0
               while (index < math.pow(2.0, featureCategories - 1).toInt - 1) {

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala

@@ -58,6 +58,6 @@ class Strategy (
     val labelWeights: Map[Int, Int] = Map[Int, Int]()) extends Serializable {
 
   require(numClassesForClassification >= 2)
-  val isMultiClassification = numClassesForClassification > 2
+  val isMulticlassClassification = numClassesForClassification > 2
 
 }