Removed tree/model/Filter.scala since no longer used. Removed debuggi…

…ng println calls in DecisionTree.scala.

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

@@ -62,7 +62,6 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
 
     timer.start("total")
 
-    // Cache input RDD for speedup during multiple passes.
     timer.start("init")
     val retaggedInput = input.retag(classOf[LabeledPoint])
     logDebug("algo = " + strategy.algo)
@@ -77,17 +76,15 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     logDebug("numBins = " + numBins)
 
     timer.start("init")
+    // Bin feature values (TreePoint representation).
+    // Cache input RDD for speedup during multiple passes.
     val treeInput = TreePoint.convertToTreeRDD(retaggedInput, strategy, bins).cache()
     timer.stop("init")
 
     // depth of the decision tree
     val maxDepth = strategy.maxDepth
     // the max number of nodes possible given the depth of the tree
     val maxNumNodes = math.pow(2, maxDepth + 1).toInt - 1
-    // Initialize an array to hold filters applied to points for each node.
-    //val filters = new Array[List[Filter]](maxNumNodes)
-    // The filter at the top node is an empty list.
-    //filters(0) = List()
     // Initialize an array to hold parent impurity calculations for each node.
     val parentImpurities = new Array[Double](maxNumNodes)
     // dummy value for top node (updated during first split calculation)
@@ -118,9 +115,8 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     /*
      * The main idea here is to perform level-wise training of the decision tree nodes thus
      * reducing the passes over the data from l to log2(l) where l is the total number of nodes.
-     * Each data sample is checked for validity w.r.t to each node at a given level -- i.e.,
-     * the sample is only used for the split calculation at the node if the sampled would have
-     * still survived the filters of the parent nodes.
+     * Each data sample is handled by a particular node at that level (or it reaches a leaf
+     * beforehand and is not used in later levels.
      */
 
     var level = 0
@@ -169,7 +165,6 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
       }
       require(math.pow(2, level) == splitsStatsForLevel.length)
       // Check whether all the nodes at the current level at leaves.
-      println(s"LOOP over levels: level=$level, splitStats...gains: ${splitsStatsForLevel.map(_._2.gain).mkString(",")}")
       val allLeaf = splitsStatsForLevel.forall(_._2.gain <= 0)
       logDebug("all leaf = " + allLeaf)
       if (allLeaf) {
@@ -237,8 +232,6 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
       logDebug("nodeIndex = " + nodeIndex + ", impurity = " + impurity)
       // noting the parent impurities
       parentImpurities(nodeIndex) = impurity
-      // noting the parents filters for the child nodes
-      val childFilter = new Filter(nodeSplitStats._1, if (i == 0) -1 else 1)
       i += 1
     }
   }
@@ -461,7 +454,6 @@ object DecisionTree extends Serializable with Logging {
    * @param maxLevelForSingleGroup the deepest level for single-group level-wise computation.
    * @param unorderedFeatures  Set of unordered (categorical) features.
    * @return array (over nodes) of splits with best split for each node at a given level.
-   *                TODO: UPDATE DOC
    */
   protected[tree] def findBestSplits(
       input: RDD[TreePoint],
@@ -512,7 +504,6 @@ object DecisionTree extends Serializable with Logging {
    * @param numGroups total number of node groups at the current level. Default value is set to 1.
    * @param groupIndex index of the node group being processed. Default value is set to 0.
    * @return array of splits with best splits for all nodes at a given level.
-   *                TODO: UPDATE DOC
    */
   private def findBestSplitsPerGroup(
       input: RDD[TreePoint],
@@ -539,7 +530,7 @@ object DecisionTree extends Serializable with Logging {
      * We use a bin-wise best split computation strategy instead of a straightforward best split
      * computation strategy. Instead of analyzing each sample for contribution to the left/right
      * child node impurity of every split, we first categorize each feature of a sample into a
-     * bin. Each bin is an interval between a low and high split. Since each splits, and thus bin,
+     * bin. Each bin is an interval between a low and high split. Since each split, and thus bin,
      * is ordered (read ordering for categorical variables in the findSplitsBins method),
      * we exploit this structure to calculate aggregates for bins and then use these aggregates
      * to calculate information gain for each split.
@@ -660,7 +651,6 @@ object DecisionTree extends Serializable with Logging {
      *             numClasses * numBins * numFeatures * numNodes.
      *             Indexed by (node, feature, bin, label) where label is the least significant bit.
      * @param nodeIndex  Node corresponding to treePoint. Indexed from 0 at start of (level, group).
-     * TODO: UPDATE DOC
      */
     def updateBinForOrderedFeature(
         treePoint: TreePoint,
@@ -681,21 +671,19 @@ object DecisionTree extends Serializable with Logging {
      * where [bins] ranges over all bins.
      * Updates left or right side of aggregate depending on split.
      *
+     * @param nodeIndex  Node corresponding to treePoint. Indexed from 0 at start of (level, group).
      * @param treePoint  Data point being aggregated.
      * @param agg  Indexed by (left/right, node, feature, bin, label)
      *             where label is the least significant bit.
      *             The left/right specifier is a 0/1 index indicating left/right child info.
      * @param rightChildShift Offset for right side of agg.
-     * TODO: UPDATE DOC
-     * TODO: Make arg order same as for ordered feature.
      */
     def updateBinForUnorderedFeature(
         nodeIndex: Int,
         featureIndex: Int,
         treePoint: TreePoint,
         agg: Array[Double],
         rightChildShift: Int): Unit = {
-      //println(s"-- updateBinForUnorderedFeature node:$nodeIndex, feature:$featureIndex, label:$label.")
       val featureValue = treePoint.features(featureIndex)
       // Update the left or right count for one bin.
       val aggShift =
@@ -780,7 +768,6 @@ object DecisionTree extends Serializable with Logging {
      * @return agg
      */
     def regressionBinSeqOp(agg: Array[Double], treePoint: TreePoint, nodeIndex: Int): Unit = {
-      // TODO: Move stuff outside loop.
       val label = treePoint.label
       // Iterate over all features.
       var featureIndex = 0
@@ -791,9 +778,6 @@ object DecisionTree extends Serializable with Logging {
           3 * numBins * numFeatures * nodeIndex +
           3 * numBins * featureIndex +
           3 * binIndex
-        if (aggIndex >= agg.size) {
-          println(s"aggIndex = $aggIndex, agg.size = ${agg.size}.  binIndex = $binIndex, featureIndex = $featureIndex, nodeIndex = $nodeIndex, numBins = $numBins, numFeatures = $numFeatures")
-        }
         agg(aggIndex) = agg(aggIndex) + 1
         agg(aggIndex + 1) = agg(aggIndex + 1) + label
         agg(aggIndex + 2) = agg(aggIndex + 2) + label * label
@@ -1025,7 +1009,6 @@ object DecisionTree extends Serializable with Logging {
        *                     Element i (i = 1, ..., numSplits - 1) is set to be
        *                     the cumulative sum (from right) over binData for bins
        *                     numBins - 1, ..., numBins - 1 - i.
-       * TODO: We could avoid doing one of these cumulative sums.
        */
       def findAggForOrderedFeatureClassification(
           leftNodeAgg: Array[Array[Array[Double]]],
@@ -1196,16 +1179,6 @@ object DecisionTree extends Serializable with Logging {
         } else {
           featureCategories
         }
-        /*
-        val isSpaceSufficientForAllCategoricalSplits =
-          numBins > math.pow(2, featureCategories.toInt - 1) - 1
-        if (isMulticlassClassification && isSpaceSufficientForAllCategoricalSplits) {
-          math.pow(2.0, featureCategories - 1).toInt - 1
-        } else {
-          // Ordered features
-          featureCategories
-        }
-        */
       }
     }