minor cleanup

Signed-off-by: Manish Amde <[email protected]>

Author: manishamde

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

@@ -41,7 +41,6 @@ class DecisionTree(val strategy : Strategy) extends Serializable with Logging {
     logDebug("numSplits = " + bins(0).length)
     strategy.numBins = bins(0).length
 
-    //TODO: Level-wise training of tree and obtain Decision Tree model
     val maxDepth = strategy.maxDepth
 
     val maxNumNodes = scala.math.pow(2,maxDepth).toInt - 1
@@ -62,7 +61,6 @@ class DecisionTree(val strategy : Strategy) extends Serializable with Logging {
         logDebug("#####################################")
 
         //Find best split for all nodes at a level
-        val numNodes= scala.math.pow(2,level).toInt
         val splitsStatsForLevel = DecisionTree.findBestSplits(input, parentImpurities, strategy, level, filters,splits,bins)
 
         for ((nodeSplitStats, index) <- splitsStatsForLevel.view.zipWithIndex){
@@ -105,7 +103,7 @@ class DecisionTree(val strategy : Strategy) extends Serializable with Logging {
   private def extractInfoForLowerLevels(level: Int, index: Int, maxDepth: Int, nodeSplitStats: (Split, InformationGainStats), parentImpurities: Array[Double], filters: Array[List[Filter]]) {
     for (i <- 0 to 1) {
 
-      val nodeIndex = (scala.math.pow(2, level + 1)).toInt - 1 + 2 * index + i
+      val nodeIndex = scala.math.pow(2, level + 1).toInt - 1 + 2 * index + i
 
       if (level < maxDepth - 1) {
 
@@ -205,7 +203,6 @@ object DecisionTree extends Serializable with Logging {
     def findBin(featureIndex: Int, labeledPoint: LabeledPoint, isFeatureContinuous : Boolean) : Int = {
 
       if (isFeatureContinuous){
-        //TODO: Do binary search
         for (binIndex <- 0 until strategy.numBins) {
           val bin = bins(featureIndex)(binIndex)
           val lowThreshold = bin.lowSplit.threshold
@@ -250,9 +247,12 @@ object DecisionTree extends Serializable with Logging {
         val shift = 1 + numFeatures * nodeIndex
         if (!sampleValid) {
           //Add to invalid bin index -1
-          for (featureIndex <- 0 until numFeatures) {
-            arr(shift+featureIndex) = -1
-            //TODO: Break since marking one bin is sufficient
+          breakable {
+            for (featureIndex <- 0 until numFeatures) {
+              arr(shift+featureIndex) = -1
+              //Breaking since marking one bin is sufficient
+              break()
+            }
           }
         } else {
           for (featureIndex <- 0 until numFeatures) {
@@ -318,7 +318,6 @@ object DecisionTree extends Serializable with Logging {
     def binSeqOp(agg : Array[Double], arr: Array[Double]) : Array[Double] = {
       strategy.algo match {
         case Classification => classificationBinSeqOp(arr, agg)
-          //TODO: Implement this
         case Regression => regressionBinSeqOp(arr, agg)
       }
       agg
@@ -599,7 +598,6 @@ object DecisionTree extends Serializable with Logging {
 
     logDebug("maxBins = " + numBins)
     //Calculate the number of sample for approximate quantile calculation
-    //TODO: Justify this calculation
     val requiredSamples = numBins*numBins
     val fraction = if (requiredSamples < count) requiredSamples.toDouble / count else 1.0
     logDebug("fraction of data used for calculating quantiles = " + fraction)
@@ -624,7 +622,6 @@ object DecisionTree extends Serializable with Logging {
             val stride : Double = numSamples.toDouble/numBins
             logDebug("stride = " + stride)
             for (index <- 0 until numBins-1) {
-              //TODO: Investigate this
               val sampleIndex = (index+1)*stride.toInt
               val split = new Split(featureIndex,featureSamples(sampleIndex),Continuous, List())
               splits(featureIndex)(index) = split