Github user MechCoder commented on a diff in the pull request:
https://github.com/apache/spark/pull/4906#discussion_r26551383
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
---
@@ -108,6 +110,58 @@ class GradientBoostedTreesModel(
}
override protected def formatVersion: String =
TreeEnsembleModel.SaveLoadV1_0.thisFormatVersion
+
+ /**
+ * Method to compute error or loss for every iteration of gradient
boosting.
+ * @param data RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]
+ * @param loss evaluation metric.
+ * @return an array with index i having the losses or errors for the
ensemble
+ * containing trees 1 to i + 1
+ */
+ def evaluateEachIteration(
+ data: RDD[LabeledPoint],
+ loss: Loss): Array[Double] = {
+
+ val sc = data.sparkContext
+ val remappedData = algo match {
+ case Classification => data.map(x => new LabeledPoint((x.label * 2)
- 1, x.features))
+ case _ => data
+ }
+
+ val numIterations = trees.length
+ val evaluationArray = Array.fill(numIterations)(0.0)
+
+ var predictionAndError: RDD[(Double, Double)] = remappedData.map { i =>
+ val pred = treeWeights(0) * trees(0).predict(i.features)
+ val error = loss.computeError(pred, i.label)
+ (pred, error)
+ }
+ evaluationArray(0) = predictionAndError.values.mean()
+
+ // Avoid the model being copied across numIterations.
+ val broadcastTrees = sc.broadcast(trees)
+ val broadcastWeights = sc.broadcast(treeWeights)
+
+ (1 until numIterations).map { nTree =>
+ predictionAndError =
remappedData.zip(predictionAndError).mapPartitions { iter =>
+ val currentTree = broadcastTrees.value(nTree)
+ val currentTreeWeight = broadcastWeights.value(nTree)
+ iter.map {
+ case (point, (pred, error)) => {
+ val newPred = pred + currentTree.predict(point.features) *
currentTreeWeight
--- End diff --
I think this is more of a design problem. Do we want
`evaluateEachIteration` to do the same thing as what the
`GradientBoostingModel` does internally (since the algo is set to Regression
explicitly)? I also think it might be confusing if users see that during
classification problems, this method behaves in a different way as compared to
internally.
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