changing instance format to weighted labeled point

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

@@ -28,6 +28,7 @@ import org.apache.spark.mllib.tree.impurity.Impurity
 import org.apache.spark.mllib.tree.model._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.random.XORShiftRandom
+import org.apache.spark.mllib.point.WeightedLabeledPoint
 
 /**
  * :: Experimental ::
@@ -47,13 +48,16 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
    */
   def train(input: RDD[LabeledPoint]): DecisionTreeModel = {
 
+    // Converting from standard instance format to weighted input format for tree training
+    val weightedInput = input.map(x => WeightedLabeledPoint(x.label,x.features))
+
     // Cache input RDD for speedup during multiple passes.
-    input.cache()
+    weightedInput.cache()
     logDebug("algo = " + strategy.algo)
 
     // Find the splits and the corresponding bins (interval between the splits) using a sample
     // of the input data.
-    val (splits, bins) = DecisionTree.findSplitsBins(input, strategy)
+    val (splits, bins) = DecisionTree.findSplitsBins(weightedInput, strategy)
     val numBins = bins(0).length
     logDebug("numBins = " + numBins)
 
@@ -70,7 +74,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     // dummy value for top node (updated during first split calculation)
     val nodes = new Array[Node](maxNumNodes)
     // num features
-    val numFeatures = input.take(1)(0).features.size
+    val numFeatures = weightedInput.take(1)(0).features.size
 
     // Calculate level for single group construction
 
@@ -109,8 +113,8 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
       logDebug("#####################################")
 
       // Find best split for all nodes at a level.
-      val splitsStatsForLevel = DecisionTree.findBestSplits(input, parentImpurities, strategy,
-        level, filters, splits, bins, maxLevelForSingleGroup)
+      val splitsStatsForLevel = DecisionTree.findBestSplits(weightedInput, parentImpurities,
+        strategy, level, filters, splits, bins, maxLevelForSingleGroup)
 
       for ((nodeSplitStats, index) <- splitsStatsForLevel.view.zipWithIndex) {
         // Extract info for nodes at the current level.
@@ -291,7 +295,7 @@ object DecisionTree extends Serializable with Logging {
    * @return array of splits with best splits for all nodes at a given level.
    */
   protected[tree] def findBestSplits(
-      input: RDD[LabeledPoint],
+      input: RDD[WeightedLabeledPoint],
       parentImpurities: Array[Double],
       strategy: Strategy,
       level: Int,
@@ -339,7 +343,7 @@ object DecisionTree extends Serializable with Logging {
    * @return array of splits with best splits for all nodes at a given level.
    */
   private def findBestSplitsPerGroup(
-      input: RDD[LabeledPoint],
+      input: RDD[WeightedLabeledPoint],
       parentImpurities: Array[Double],
       strategy: Strategy,
       level: Int,
@@ -399,7 +403,7 @@ object DecisionTree extends Serializable with Logging {
      * Find whether the sample is valid input for the current node, i.e., whether it passes through
      * all the filters for the current node.
      */
-    def isSampleValid(parentFilters: List[Filter], labeledPoint: LabeledPoint): Boolean = {
+    def isSampleValid(parentFilters: List[Filter], labeledPoint: WeightedLabeledPoint): Boolean = {
       // leaf
       if ((level > 0) & (parentFilters.length == 0)) {
         return false
@@ -438,7 +442,7 @@ object DecisionTree extends Serializable with Logging {
      */
     def findBin(
         featureIndex: Int,
-        labeledPoint: LabeledPoint,
+        labeledPoint: WeightedLabeledPoint,
         isFeatureContinuous: Boolean): Int = {
       val binForFeatures = bins(featureIndex)
       val feature = labeledPoint.features(featureIndex)
@@ -509,7 +513,7 @@ object DecisionTree extends Serializable with Logging {
      * where b_ij is an integer between 0 and numBins - 1.
      * Invalid sample is denoted by noting bin for feature 1 as -1.
      */
-    def findBinsForLevel(labeledPoint: LabeledPoint): Array[Double] = {
+    def findBinsForLevel(labeledPoint: WeightedLabeledPoint): Array[Double] = {
       // Calculate bin index and label per feature per node.
       val arr = new Array[Double](1 + (numFeatures * numNodes))
       arr(0) = labeledPoint.label
@@ -982,7 +986,7 @@ object DecisionTree extends Serializable with Logging {
    *         .spark.mllib.tree.model.Bin] of size (numFeatures, numSplits1)
    */
   protected[tree] def findSplitsBins(
-      input: RDD[LabeledPoint],
+      input: RDD[WeightedLabeledPoint],
       strategy: Strategy): (Array[Array[Split]], Array[Array[Bin]]) = {
     val count = input.count()