imatiach-msft commented on a change in pull request #25926: [SPARK-9612][ML]
Add instance weight support for GBTs
URL: https://github.com/apache/spark/pull/25926#discussion_r335027506
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File path:
mllib/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTrees.scala
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@@ -166,29 +163,50 @@ private[spark] object GradientBoostedTrees extends
Logging {
* Method to calculate error of the base learner for the gradient boosting
calculation.
* Note: This method is not used by the gradient boosting algorithm but is
useful for debugging
* purposes.
- * @param data Training dataset: RDD of `LabeledPoint`.
+ * @param data Training dataset: RDD of `Instance`.
* @param trees Boosted Decision Tree models
* @param treeWeights Learning rates at each boosting iteration.
* @param loss evaluation metric.
* @return Measure of model error on data
*/
def computeError(
- data: RDD[LabeledPoint],
+ data: RDD[Instance],
trees: Array[DecisionTreeRegressionModel],
treeWeights: Array[Double],
loss: OldLoss): Double = {
- data.map { lp =>
+ val (errSum, weightSum) = data.map { case Instance(label, weight,
features) =>
val predicted = trees.zip(treeWeights).foldLeft(0.0) { case (acc,
(model, weight)) =>
- updatePrediction(lp.features, acc, model, weight)
+ updatePrediction(features, acc, model, weight)
}
- loss.computeError(predicted, lp.label)
- }.mean()
+ (loss.computeError(predicted, label) * weight, weight)
+ }.treeReduce{ case ((err1, weight1), (err2, weight2)) =>
+ (err1 + err2, weight1 + weight2)
+ }
+ errSum / weightSum
+ }
+
+ /**
+ * Method to calculate error of the base learner for the gradient boosting
calculation.
+ * @param data Training dataset: RDD of `Instance`.
+ * @param predError Prediction and error.
+ * @return Measure of model error on data
+ */
+ def computeError(
Review comment:
maybe change the name to computeWeightedError to make that clear, since the
above methods are also computing error but unweighted
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