Github user codedeft commented on a diff in the pull request:
https://github.com/apache/spark/pull/2868#discussion_r19195671
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -553,7 +589,26 @@ object DecisionTree extends Serializable with Logging {
// Finally, only best Splits for nodes are collected to driver to
construct decision tree.
val nodeToFeatures = getNodeToFeatures(treeToNodeToIndexInfo)
val nodeToFeaturesBc = input.sparkContext.broadcast(nodeToFeatures)
- val nodeToBestSplits =
+
+ val partitionAggregates = if (useNodeIdCache) {
+ input.zip(nodeIdCache.get.cur).mapPartitions { points =>
+ // Construct a nodeStatsAggregators array to hold node aggregate
stats,
+ // each node will have a nodeStatsAggregator
+ val nodeStatsAggregators = Array.tabulate(numNodes) { nodeIndex =>
+ val featuresForNode = nodeToFeaturesBc.value.flatMap {
nodeToFeatures =>
+ Some(nodeToFeatures(nodeIndex))
+ }
+ new DTStatsAggregator(metadata, featuresForNode)
+ }
+
+ // iterator all instances in current partition and update
aggregate stats
+ points.foreach(binSeqOpWithNodeIdCache(nodeStatsAggregators, _))
--- End diff --
Well, one requires zip and the other one doesn't, so fundamentally changes
the type of rows.
Additionally, I think if we just branch out within mapPartitions, won't it
unnecessarily serialize some things that are not used in one branch and not the
other? E.g. it seems that binSeqOp itself becomes object and will be
serialized, along with closure items.
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