Skip to content

[SPARK-59402][ML] Reduce RandomForestRegressionModel broadcast size #58695

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
zhengruifeng wants to merge 2 commits into from
+18 −3
Closed

[SPARK-59402][ML] Reduce RandomForestRegressionModel broadcast size #58695

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
65e4f59
[SPARK-59402][ML] Reduce RandomForestRegressionModel broadcast size
zhengruifeng Sep 10, 2026
32829d0
[SPARK-59402][ML] Reuse tree ensemble prediction helper
zhengruifeng Sep 11, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
11 changes: 8 additions & 3 deletions mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -238,17 +238,22 @@ class RandomForestRegressionModel private[ml] (
if ($(predictionCol).nonEmpty || $(leafCol).nonEmpty) {
var predColNames = Seq.empty[String]
var predCols = Seq.empty[Column]
val bcModel = dataset.sparkSession.sparkContext.broadcast(this)
val bcRootNodes = dataset.sparkSession.sparkContext.broadcast(_trees.map(_.rootNode))

if ($(predictionCol).nonEmpty) {
val predUDF = udf { features: Vector => bcModel.value.predict(features) }
val predUDF = udf { features: Vector =>
val rootNodes = bcRootNodes.value
TreeEnsembleModel.predictRaw(features, rootNodes) / rootNodes.length
}
predColNames :+= $(predictionCol)
predCols :+= predUDF(col($(featuresCol)))
.as($(predictionCol), outputSchema($(predictionCol)).metadata)
}

if ($(leafCol).nonEmpty) {
val leafUDF = udf { features: Vector => bcModel.value.predictLeaf(features) }
val leafUDF = udf { features: Vector =>
TreeEnsembleModel.predictLeaf(features, bcRootNodes.value)
}
predColNames :+= $(leafCol)
predCols :+= leafUDF(col($(featuresCol)))
.as($(leafCol), outputSchema($(leafCol)).metadata)
Expand Down
10 changes: 10 additions & 0 deletions mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -207,6 +207,16 @@ private[ml] object TreeEnsembleModel {
prediction
}

private[ml] def predictRaw(features: Vector, rootNodes: Array[Node]): Double = {
var prediction = 0.0
var i = 0
while (i < rootNodes.length) {
prediction += rootNodes(i).predictImpl(features).prediction
i += 1
}
prediction
}

private[ml] def predictLeaf(features: Vector, rootNodes: Array[Node]): Vector = {
val indices = Array.ofDim[Double](rootNodes.length)
var i = 0
Expand Down