Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5330#discussion_r27700338
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
---
@@ -166,6 +156,56 @@ class GradientBoostedTreesModel(
object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel]
{
+ /**
+ * Method to compute initial error and prediction as a RDD for the first
+ * iteration of gradient boosting.
+ * @param data RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]
+ * @param initTreeWeight: learning rate assigned to the first tree.
+ * @param initTree: first DecisionTreeModel
+ * @param loss: evaluation metric
+ * @return a RDD with each element being a zip of the prediction and
error
+ * corresponding to every sample.
+ */
+ def computeInitialPredictionAndError(
+ data: RDD[LabeledPoint],
+ initTreeWeight: Double,
+ initTree: DecisionTreeModel, loss: Loss): RDD[(Double, Double)] = {
+ data.map { i =>
+ val pred = initTreeWeight * initTree.predict(i.features)
+ val error = loss.computeError(pred, i.label)
+ (pred, error)
+ }
+ }
+
+ /**
+ * Method to update a zipped predictionError RDD
+ * (as obtained with computeInitialPredictionAndError)
+ * @param data RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]
+ * @param predictionAndError: predictionError RDD
+ * @param currentTreeWeight: learning rate.
+ * @param currentTree: first DecisionTree
+ * @param loss: evaluation metric
+ * @return a RDD with each element being a zip of the prediction and
error
+ * corresponing to each sample.
+ */
+ def updatePredictionError(
+ data: RDD[LabeledPoint],
+ predictionAndError: RDD[(Double, Double)],
+ currentTreeWeight: Double,
+ currentTree: DecisionTreeModel,
+ loss: Loss): RDD[(Double, Double)] = {
+
+ data.zip(predictionAndError).mapPartitions { iter =>
+ iter.map {
+ case (point, (pred, error)) => {
--- End diff --
"point" --> (Use the same variable name for a labeled point everywhere for
consistency. I tend to use "lp")
---
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