additional code for creating intermediate RDD

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

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

@@ -37,7 +37,6 @@ class DecisionTree(val strategy : Strategy) {
     val (splits, bins) = DecisionTree.find_splits_bins(input, strategy)
 
     //TODO: Level-wise training of tree and obtain Decision Tree model
-
     val maxDepth = strategy.maxDepth
 
     val maxNumNodes = scala.math.pow(2,maxDepth).toInt - 1
@@ -55,8 +54,20 @@ class DecisionTree(val strategy : Strategy) {
 
 }
 
-object DecisionTree extends Logging {
+object DecisionTree extends Serializable {
+
+  /*
+  Returns an Array[Split] of optimal splits for all nodes at a given level
+
+  @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data for DecisionTree
+  @param strategy [[org.apache.spark.mllib.tree.Strategy]] instance containing parameters for construction the DecisionTree
+  @param level Level of the tree
+  @param filters Filter for all nodes at a given level
+  @param splits possible splits for all features
+  @param bins possible bins for all features
 
+  @return Array[Split] instance for best splits for all nodes at a given level.
+    */
   def findBestSplits(
                       input : RDD[LabeledPoint],
                       strategy: Strategy,
@@ -65,6 +76,16 @@ object DecisionTree extends Logging {
                       splits : Array[Array[Split]],
                       bins : Array[Array[Bin]]) : Array[Split] = {
 
+    //TODO: Move these calculations outside
+    val numNodes = scala.math.pow(2, level).toInt
+    println("numNodes = " + numNodes)
+    //Find the number of features by looking at the first sample
+    val numFeatures = input.take(1)(0).features.length
+    println("numFeatures = " + numFeatures)
+    val numSplits = strategy.numSplits
+    println("numSplits = " + numSplits)
+
+    /*Find the filters used before reaching the current code*/
     def findParentFilters(nodeIndex: Int): List[Filter] = {
       if (level == 0) {
         List[Filter]()
@@ -75,6 +96,10 @@ object DecisionTree extends Logging {
       }
     }
 
+    /*Find whether the sample is valid input for the current node.
+
+    In other words, does it pass through all the filters for the current node.
+    */
     def isSampleValid(parentFilters: List[Filter], labeledPoint: LabeledPoint): Boolean = {
 
       for (filter <- parentFilters) {
@@ -91,79 +116,130 @@ object DecisionTree extends Logging {
       true
     }
 
+    /*Finds the right bin for the given feature*/
     def findBin(featureIndex: Int, labeledPoint: LabeledPoint) : Int = {
-
       //TODO: Do binary search
       for (binIndex <- 0 until strategy.numSplits) {
         val bin = bins(featureIndex)(binIndex)
-        //TODO: Remove this requirement post basic functional testing
-        require(bin.lowSplit.feature == featureIndex)
-        require(bin.highSplit.feature == featureIndex)
+        //TODO: Remove this requirement post basic functional
         val lowThreshold = bin.lowSplit.threshold
         val highThreshold = bin.highSplit.threshold
         val features = labeledPoint.features
-        if ((lowThreshold < features(featureIndex)) & (highThreshold < features(featureIndex))) {
+        if ((lowThreshold <= features(featureIndex)) & (highThreshold > features(featureIndex))) {
           return binIndex
         }
       }
       throw new UnknownError("no bin was found.")
 
     }
-    def findBinsForLevel: Array[Double] = {
 
-      val numNodes = scala.math.pow(2, level).toInt
-      //Find the number of features by looking at the first sample
-      val numFeatures = input.take(1)(0).features.length
+    /*Finds bins for all nodes (and all features) at a given level
+     k features, l nodes
+     Storage label, b11, b12, b13, .., bk, b21, b22, .. ,bl1, bl2, .. ,blk
+     Denotes invalid sample for tree by noting bin for feature 1 as -1
+    */
+    def findBinsForLevel(labeledPoint : LabeledPoint) : Array[Double] = {
+
 
-      //TODO: Bit pack more by removing redundant label storage
       // calculating bin index and label per feature per node
-      val arr = new Array[Double](2 * numFeatures * numNodes)
+      val arr = new Array[Double](1+(numFeatures * numNodes))
+      arr(0) = labeledPoint.label
       for (nodeIndex <- 0 until numNodes) {
         val parentFilters = findParentFilters(nodeIndex)
         //Find out whether the sample qualifies for the particular node
         val sampleValid = isSampleValid(parentFilters, labeledPoint)
-        val shift = 2 * numFeatures * nodeIndex
-        if (sampleValid) {
+        val shift = 1 + numFeatures * nodeIndex
+        if (!sampleValid) {
           //Add to invalid bin index -1
-          for (featureIndex <- shift until (shift + numFeatures) by 2) {
-            arr(featureIndex + 1) = -1
-            arr(featureIndex + 2) = labeledPoint.label
+          for (featureIndex <- 0 until numFeatures) {
+            arr(shift+featureIndex) = -1
+            //TODO: Break since marking one bin is sufficient
           }
         } else {
           for (featureIndex <- 0 until numFeatures) {
-            arr(shift + (featureIndex * 2) + 1) = findBin(featureIndex, labeledPoint)
-            arr(shift + (featureIndex * 2) + 2) = labeledPoint.label
+            //println("shift+featureIndex =" + (shift+featureIndex))
+            arr(shift + featureIndex) = findBin(featureIndex, labeledPoint)
           }
         }
 
       }
       arr
     }
 
-    val binMappedRDD = input.map(labeledPoint => findBinsForLevel)
+    /*
+    Performs a sequential aggreation over a partition
+
+    @param agg Array[Double] storing aggregate calculation of size numSplits*numFeatures*numNodes for classification
+    and 3*numSplits*numFeatures*numNodes for regression
+    @param arr Array[Double] of size 1+(numFeatures*numNodes)
+    @return Array[Double] storing aggregate calculation of size numSplits*numFeatures*numNodes for classification
+    and 3*numSplits*numFeatures*numNodes for regression
+     */
+    def binSeqOp(agg : Array[Double], arr: Array[Double]) : Array[Double] = {
+      for (node <- 0 until numNodes) {
+         val validSignalIndex = 1+numFeatures*node
+         val isSampleValidForNode = if(arr(validSignalIndex) != -1) true else false
+         if(isSampleValidForNode) {
+           for (feature <- 0 until numFeatures){
+               val arrShift = 1 + numFeatures*node
+               val aggShift = numSplits*numFeatures*node
+               val arrIndex = arrShift + feature
+               val aggIndex = aggShift + feature*numSplits + arr(arrIndex).toInt
+               agg(aggIndex) =  agg(aggIndex) + 1
+           }
+         }
+      }
+      agg
+    }
+
+    def binCombOp(par1 : Array[Double], par2: Array[Double]) : Array[Double] = {
+      par1
+    }
+
+    println("input = " + input.count)
+    val binMappedRDD = input.map(x => findBinsForLevel(x))
+    println("binMappedRDD.count = " + binMappedRDD.count)
     //calculate bin aggregates
+
+    val binAggregates = binMappedRDD.aggregate(Array.fill[Double](numSplits*numFeatures*numNodes)(0))(binSeqOp,binCombOp)
+
     //find best split
+    println("binAggregates.length = " + binAggregates.length)
 
 
-    Array[Split]()
+    val bestSplits = new Array[Split](numNodes)
+    for (node <- 0 until numNodes){
+       val binsForNode = binAggregates.slice(node,numSplits*node)
+    }
+
+    bestSplits
   }
 
+  /*
+  Returns split and bins for decision tree calculation.
+
+  @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data for DecisionTree
+  @param strategy [[org.apache.spark.mllib.tree.Strategy]] instance containing parameters for construction the DecisionTree
+  @return a tuple of (splits,bins) where Split is an Array[Array[Split]] of size (numFeatures,numSplits-1) and bins is an
+   Array[Array[Bin]] of size (numFeatures,numSplits1)
+   */
   def find_splits_bins(input : RDD[LabeledPoint], strategy : Strategy) : (Array[Array[Split]], Array[Array[Bin]]) = {
 
     val numSplits = strategy.numSplits
-    logDebug("numSplits = " + numSplits)
+    println("numSplits = " + numSplits)
 
     //Calculate the number of sample for approximate quantile calculation
     //TODO: Justify this calculation
     val requiredSamples = numSplits*numSplits
     val count = input.count()
     val fraction = if (requiredSamples < count) requiredSamples.toDouble / count else 1.0
-    logDebug("fraction of data used for calculating quantiles = " + fraction)
+    println("fraction of data used for calculating quantiles = " + fraction)
 
     //sampled input for RDD calculation
     val sampledInput = input.sample(false, fraction, 42).collect()
     val numSamples = sampledInput.length
 
+    //TODO: Remove this requirement
     require(numSamples > numSplits, "length of input samples should be greater than numSplits")
 
     //Find the number of features by looking at the first sample

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

@@ -18,7 +18,7 @@ package org.apache.spark.mllib.tree
 
 import org.apache.spark.mllib.tree.impurity.Impurity
 
-class Strategy (
+case class Strategy (
                 val kind : String,
                 val impurity : Impurity,
                 val maxDepth : Int,

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala

@@ -16,7 +16,7 @@
  */
 package org.apache.spark.mllib.tree.impurity
 
-trait Impurity {
+trait Impurity extends Serializable {
 
   def calculate(c0 : Double, c1 : Double): Double
 

mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala

@@ -28,6 +28,7 @@ import org.jblas._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.impurity.Gini
+import org.apache.spark.mllib.tree.model.Filter
 
 class DecisionTreeSuite extends FunSuite with BeforeAndAfterAll {
 
@@ -54,6 +55,23 @@ class DecisionTreeSuite extends FunSuite with BeforeAndAfterAll {
     assert(bins(0).length==100)
     println(splits(1)(98))
   }
+
+  test("stump"){
+    val arr = DecisionTreeSuite.generateReverseOrderedLabeledPoints()
+    assert(arr.length == 1000)
+    val rdd = sc.parallelize(arr)
+    val strategy = new Strategy("regression",Gini,3,100,"sort")
+    val (splits, bins) = DecisionTree.find_splits_bins(rdd,strategy)
+    assert(splits.length==2)
+    assert(splits(0).length==99)
+    assert(bins.length==2)
+    assert(bins(0).length==100)
+    assert(splits(0).length==99)
+    assert(bins(0).length==100)
+    println(splits(1)(98))
+    DecisionTree.findBestSplits(rdd,strategy,0,Array[List[Filter]](),splits,bins)
+  }
+
 }
 
 object DecisionTreeSuite {