[GitHub] [spark] zhengruifeng commented on a change in pull request #25926: [SPARK-9612][ML] Add instance weight support for GBTs
zhengruifeng 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_r332326534
##
File path:
mllib/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTrees.scala
##
@@ -197,41 +215,41 @@ private[spark] object GradientBoostedTrees extends
Logging {
* containing the first i+1 trees
*/
def evaluateEachIteration(
- data: RDD[LabeledPoint],
+ data: RDD[Instance],
trees: Array[DecisionTreeRegressionModel],
treeWeights: Array[Double],
loss: OldLoss,
algo: OldAlgo.Value): Array[Double] = {
val sc = data.sparkContext
val remappedData = algo match {
- case OldAlgo.Classification => data.map(x => new LabeledPoint((x.label *
2) - 1, x.features))
+ case OldAlgo.Classification =>
+data.map(x => Instance((x.label * 2) - 1, x.weight, x.features))
case _ => data
}
val broadcastTrees = sc.broadcast(trees)
val localTreeWeights = treeWeights
-val treesIndices = trees.indices
-
-val dataCount = remappedData.count()
-val evaluation = remappedData.map { point =>
- treesIndices.map { idx =>
-val prediction = broadcastTrees.value(idx)
- .rootNode
- .predictImpl(point.features)
- .prediction
-prediction * localTreeWeights(idx)
+val numTrees = trees.length
+
+val (errSum, weightSum) = remappedData.mapPartitions { iter =>
Review comment:
in this place, I just followed preivous impl. I am neutral on it.
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[GitHub] [spark] zhengruifeng commented on a change in pull request #25926: [SPARK-9612][ML] Add instance weight support for GBTs
zhengruifeng 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_r332320495
##
File path:
mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
##
@@ -68,7 +68,7 @@ class GradientBoostedTrees private[spark] (
def run(input: RDD[LabeledPoint]): GradientBoostedTreesModel = {
val algo = boostingStrategy.treeStrategy.algo
val (trees, treeWeights) = NewGBT.run(input.map { point =>
- NewLabeledPoint(point.label, point.features.asML)
+ NewLabeledPoint(point.label, point.features.asML).toInstance
Review comment:
Yes, it is better to directly create `Instance`
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[GitHub] [spark] zhengruifeng commented on a change in pull request #25926: [SPARK-9612][ML] Add instance weight support for GBTs
zhengruifeng 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_r328916510
##
File path:
mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
##
@@ -296,6 +304,35 @@ class GBTRegressorSuite extends MLTest with
DefaultReadWriteTest {
}
}
+ test("training with sample weights") {
+val df = linearRegressionData
+val numClasses = 0
+// (maxIter, maxDepth)
+val testParams = Seq(
+ (5, 5),
+ (5, 10)
+)
+
+for ((maxIter, maxDepth) <- testParams) {
+ val estimator = new GBTRegressor()
+.setMaxIter(maxIter)
+.setMaxDepth(maxDepth)
+.setSeed(seed)
+.setMinWeightFractionPerNode(0.1)
+
+ MLTestingUtils.testArbitrarilyScaledWeights[GBTRegressionModel,
+GBTRegressor](df.as[LabeledPoint], estimator,
+MLTestingUtils.modelPredictionEquals(df, _ ~= _ relTol 0.1, 0.95))
Review comment:
Compared to `DecisionTreeRegressorSuite`, I need to limit the number of
trees and loose the tolerance eps(0.99 -> 0.95) to pass the cases.
I wonder if it is due to accumulated errors among trees.
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