Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6300#discussion_r30766001
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
---
@@ -134,13 +136,28 @@ final class RandomForestClassificationModel
private[ml] (
override def treeWeights: Array[Double] = _treeWeights
+ override def transform(dataset: DataFrame): DataFrame = {
+ val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
+ transformImpl(dataset, bcastModel)
+ }
+
override protected def predict(features: Vector): Double = {
- // TODO: Override transform() to broadcast model. SPARK-7127
// TODO: When we add a generic Bagging class, handle transform there:
SPARK-7128
+ // Predict without using a broadcasted mode
+ predictImpl(features, () => this)
--- End diff --
Yes, we will want to. If it's a small model, then it can be faster to not
broadcast it.
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