srowen commented on a change in pull request #21632: [SPARK-19591][ML][MLlib]
Add sample weights to decision trees
URL: https://github.com/apache/spark/pull/21632#discussion_r243597203
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File path:
mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
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@@ -1002,19 +1019,20 @@ private[spark] object RandomForest extends Logging
with Serializable {
val numSplits = metadata.numSplits(featureIndex)
// get count for each distinct value except zero value
- val partNumSamples = featureSamples.size
- val partValueCountMap = scala.collection.mutable.Map[Double, Int]()
- featureSamples.foreach { x =>
- partValueCountMap(x) = partValueCountMap.getOrElse(x, 0) + 1
- }
+ val (partValueCountMap, partNumSamples) =
+ featureSamples.foldLeft((Map.empty[Double, Double], 0.0)) {
+ case ((m, cnt), (w, x)) =>
+ (m + ((x, m.getOrElse(x, 0.0) + w)), cnt + w)
+ }
// Calculate the expected number of samples for finding splits
- val numSamples = (samplesFractionForFindSplits(metadata) *
metadata.numExamples).toInt
+ val weightedNumSamples = samplesFractionForFindSplits(metadata) *
+ metadata.weightedNumExamples
// add expected zero value count and get complete statistics
- val valueCountMap: Map[Double, Int] = if (numSamples - partNumSamples >
0) {
- partValueCountMap.toMap + (0.0 -> (numSamples - partNumSamples))
+ val valueCountMap: Map[Double, Double] = if (weightedNumSamples -
partNumSamples > 1e-5) {
Review comment:
Utils.EPSILON is used elsewhere; should this tolerance be different and
would you need an abs()?
This is also easier if the map here is mutable, and you can just put a value
for 0.0.
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