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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