Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/886#discussion_r14865144
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -768,104 +973,157 @@ object DecisionTree extends Serializable with
Logging {
/**
* Extracts left and right split aggregates.
* @param binData Array[Double] of size 2*numFeatures*numSplits
- * @return (leftNodeAgg, rightNodeAgg) tuple of type (Array[Double],
- * Array[Double]) where each array is of
size(numFeature,2*(numSplits-1))
+ * @return (leftNodeAgg, rightNodeAgg) tuple of type
(Array[Array[Array[Double\]\]\],
+ * Array[Array[Array[Double\]\]\]) where each array is of
size(numFeature,
+ * (numBins - 1), numClasses)
*/
def extractLeftRightNodeAggregates(
- binData: Array[Double]): (Array[Array[Double]],
Array[Array[Double]]) = {
+ binData: Array[Double]): (Array[Array[Array[Double]]],
Array[Array[Array[Double]]]) = {
+
+
+ def findAggForOrderedFeatureClassification(
+ leftNodeAgg: Array[Array[Array[Double]]],
+ rightNodeAgg: Array[Array[Array[Double]]],
+ featureIndex: Int) {
+
+ // shift for this featureIndex
+ val shift = numClasses * featureIndex * numBins
+
+ var classIndex = 0
+ while (classIndex < numClasses) {
+ // left node aggregate for the lowest split
+ leftNodeAgg(featureIndex)(0)(classIndex) = binData(shift +
classIndex)
+ // right node aggregate for the highest split
+ rightNodeAgg(featureIndex)(numBins - 2)(classIndex)
+ = binData(shift + (numClasses * (numBins - 1)) + classIndex)
+ classIndex += 1
+ }
+
+ // Iterate over all splits.
+ var splitIndex = 1
+ while (splitIndex < numBins - 1) {
+ // calculating left node aggregate for a split as a sum of left
node aggregate of a
+ // lower split and the left bin aggregate of a bin where the
split is a high split
+ var innerClassIndex = 0
+ while (innerClassIndex < numClasses) {
+ leftNodeAgg(featureIndex)(splitIndex)(innerClassIndex)
+ = binData(shift + numClasses * splitIndex + innerClassIndex)
+
+ leftNodeAgg(featureIndex)(splitIndex - 1)(innerClassIndex)
+ rightNodeAgg(featureIndex)(numBins - 2 -
splitIndex)(innerClassIndex) =
+ binData(shift + (numClasses * (numBins - 1 - splitIndex) +
innerClassIndex)) +
+ rightNodeAgg(featureIndex)(numBins - 1 -
splitIndex)(innerClassIndex)
+ innerClassIndex += 1
+ }
+ splitIndex += 1
+ }
+ }
+
+ def findAggForUnorderedFeatureClassification(
+ leftNodeAgg: Array[Array[Array[Double]]],
+ rightNodeAgg: Array[Array[Array[Double]]],
+ featureIndex: Int) {
+
+ val rightChildShift = numClasses * numBins * numFeatures
+ var splitIndex = 0
+ while (splitIndex < numBins - 1) {
+ var classIndex = 0
+ while (classIndex < numClasses) {
+ // shift for this featureIndex
+ val shift = numClasses * featureIndex * numBins + splitIndex *
numClasses
+ val leftBinValue = binData(shift + classIndex)
+ val rightBinValue = binData(rightChildShift + shift +
classIndex)
+ leftNodeAgg(featureIndex)(splitIndex)(classIndex) =
leftBinValue
+ rightNodeAgg(featureIndex)(splitIndex)(classIndex) =
rightBinValue
+ classIndex += 1
+ }
+ splitIndex += 1
+ }
+ }
+
+ def findAggForRegression(
+ leftNodeAgg: Array[Array[Array[Double]]],
+ rightNodeAgg: Array[Array[Array[Double]]],
+ featureIndex: Int) {
+
+ // shift for this featureIndex
+ val shift = 3 * featureIndex * numBins
+ // left node aggregate for the lowest split
+ leftNodeAgg(featureIndex)(0)(0) = binData(shift + 0)
+ leftNodeAgg(featureIndex)(0)(1) = binData(shift + 1)
+ leftNodeAgg(featureIndex)(0)(2) = binData(shift + 2)
+
+ // right node aggregate for the highest split
+ rightNodeAgg(featureIndex)(numBins - 2)(0) =
+ binData(shift + (3 * (numBins - 1)))
+ rightNodeAgg(featureIndex)(numBins - 2)(1) =
+ binData(shift + (3 * (numBins - 1)) + 1)
+ rightNodeAgg(featureIndex)(numBins - 2)(2) =
+ binData(shift + (3 * (numBins - 1)) + 2)
+
+ // Iterate over all splits.
+ var splitIndex = 1
+ while (splitIndex < numBins - 1) {
+ // calculating left node aggregate for a split as a sum of left
node aggregate of a
+ // lower split and the left bin aggregate of a bin where the
split is a high split
+ leftNodeAgg(featureIndex)(splitIndex)(0) = binData(shift + 3 *
splitIndex) +
--- End diff --
Will fix this.
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