Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/2435#discussion_r18063715
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -465,43 +329,60 @@ object DecisionTree extends Serializable with Logging
{
* @param agg Array storing aggregate calculation, with a set of
sufficient statistics for
* each (node, feature, bin).
* @param treePoint Data point being aggregated.
- * @param nodeIndex Node corresponding to treePoint. Indexed from 0 at
start of (level, group).
+ * @param nodeIndex Node corresponding to treePoint. agg is indexed in
[0, numNodes).
* @param bins possible bins for all features, indexed
(numFeatures)(numBins)
* @param unorderedFeatures Set of indices of unordered features.
+ * @param instanceWeight Weight (importance) of instance in dataset.
*/
private def mixedBinSeqOp(
agg: DTStatsAggregator,
treePoint: TreePoint,
nodeIndex: Int,
bins: Array[Array[Bin]],
- unorderedFeatures: Set[Int]): Unit = {
- // Iterate over all features.
- val numFeatures = treePoint.binnedFeatures.size
+ unorderedFeatures: Set[Int],
+ instanceWeight: Double,
+ featuresForNode: Option[Array[Int]]): Unit = {
+ val numFeaturesPerNode = if (featuresForNode.nonEmpty) {
+ // Use subsampled features
+ featuresForNode.get.size
+ } else {
+ // Use all features
+ agg.metadata.numFeatures
+ }
val nodeOffset = agg.getNodeOffset(nodeIndex)
- var featureIndex = 0
- while (featureIndex < numFeatures) {
+ // Iterate over features.
+ var featureIndexIdx = 0
+ while (featureIndexIdx < numFeaturesPerNode) {
+ val featureIndex = if (featuresForNode.nonEmpty) {
+ featuresForNode.get.apply(featureIndexIdx)
--- End diff --
minor: apply is a bit awkward since you normally use the syntactic sugar ()
to access array elements.
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