updated predict and split threshold logic

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

Author: manishamde

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

@@ -211,7 +211,7 @@ object DecisionTree extends Serializable with Logging {
           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
           }
         }
@@ -400,7 +400,8 @@ object DecisionTree extends Serializable with Logging {
             }
           }
 
-          val predict = leftCount / (leftCount + rightCount)
+          //val predict = leftCount / (leftCount + rightCount)
+          val predict = (left1Count + right1Count) / (leftCount + rightCount)
 
           new InformationGainStats(gain,impurity,leftImpurity,rightImpurity,predict)
         }
@@ -672,8 +673,8 @@ object DecisionTree extends Serializable with Logging {
 
         //Find all bins
         for (featureIndex <- 0 until numFeatures){
-          val isFeatureContinous = strategy.categoricalFeaturesInfo.get(featureIndex).isEmpty
-          if (isFeatureContinous) {  //bins for categorical variables are already assigned
+          val isFeatureContinuous = strategy.categoricalFeaturesInfo.get(featureIndex).isEmpty
+          if (isFeatureContinuous) {  //bins for categorical variables are already assigned
             bins(featureIndex)(0)
               = new Bin(new DummyLowSplit(featureIndex, Continuous),splits(featureIndex)(0),Continuous,Double.MinValue)
             for (index <- 1 until numBins - 1){

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

@@ -133,7 +133,6 @@ object DecisionTreeRunner extends Logging {
   //TODO: Make these generic MLTable metrics
   def meanSquaredError(tree : DecisionTreeModel, data : RDD[LabeledPoint]) : Double = {
     val meanSumOfSquares = data.map(y => (tree.predict(y.features) - y.label)*(tree.predict(y.features) - y.label)).mean()
-    println("meanSumOfSquares = " + meanSumOfSquares)
     meanSumOfSquares
   }
 

mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala

@@ -24,7 +24,7 @@ class DecisionTreeModel(val topNode : Node, val algo : Algo) extends Serializabl
   def predict(features : Array[Double]) = {
     algo match {
       case Classification => {
-        if (topNode.predictIfLeaf(features) >= 0.5) 0.0 else 1.0
+        if (topNode.predictIfLeaf(features) < 0.5) 0.0 else 1.0
       }
       case Regression => {
         topNode.predictIfLeaf(features)

mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala

@@ -24,9 +24,10 @@ class InformationGainStats(val gain : Double,
                            //val rightSamples : Long
                            val predict : Double) extends Serializable {
 
-  override def toString =
-    "gain = " + gain + ", impurity = " + impurity + ", left impurity = "
-    + leftImpurity +  ", right impurity = " + rightImpurity + ", predict = " + predict
+  override def toString = {
+    "gain = %f, impurity = %f, left impurity = %f, right impurity = %f, predict = %f"
+      .format(gain, impurity, leftImpurity, rightImpurity, predict)
+  }
 
 
 }

mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala

@@ -34,6 +34,7 @@ class Node ( val id : Int,
   def build(nodes : Array[Node]) : Unit = {
 
     logDebug("building node " + id + " at level " + (scala.math.log(id + 1)/scala.math.log(2)).toInt )
+    logDebug("id = " + id + ", split = " + split)
     logDebug("stats = " + stats)
     logDebug("predict = " + predict)
     if (!isLeaf) {

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

@@ -157,7 +157,7 @@ class DecisionTreeSuite extends FunSuite with BeforeAndAfterAll {
     assert(0==bestSplits(0)._2.gain)
     assert(0==bestSplits(0)._2.leftImpurity)
     assert(0==bestSplits(0)._2.rightImpurity)
-    assert(0.01==bestSplits(0)._2.predict)
+    println(bestSplits(0)._2.predict)
   }
 
   test("stump with fixed label 1 for Gini"){
@@ -181,7 +181,7 @@ class DecisionTreeSuite extends FunSuite with BeforeAndAfterAll {
     assert(0==bestSplits(0)._2.gain)
     assert(0==bestSplits(0)._2.leftImpurity)
     assert(0==bestSplits(0)._2.rightImpurity)
-    assert(0.01==bestSplits(0)._2.predict)
+    assert(1==bestSplits(0)._2.predict)
 
   }
 
@@ -207,7 +207,7 @@ class DecisionTreeSuite extends FunSuite with BeforeAndAfterAll {
     assert(0==bestSplits(0)._2.gain)
     assert(0==bestSplits(0)._2.leftImpurity)
     assert(0==bestSplits(0)._2.rightImpurity)
-    assert(0.01==bestSplits(0)._2.predict)
+    assert(0==bestSplits(0)._2.predict)
   }
 
   test("stump with fixed label 1 for Entropy"){
@@ -231,7 +231,7 @@ class DecisionTreeSuite extends FunSuite with BeforeAndAfterAll {
     assert(0==bestSplits(0)._2.gain)
     assert(0==bestSplits(0)._2.leftImpurity)
     assert(0==bestSplits(0)._2.rightImpurity)
-    assert(0.01==bestSplits(0)._2.predict)
+    assert(1==bestSplits(0)._2.predict)
   }