imatiach-msft commented on a change in pull request #21632:
[SPARK-19591][ML][MLlib] Add sample weights to decision trees
URL: https://github.com/apache/spark/pull/21632#discussion_r250233505
##########
File path: mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
##########
@@ -268,4 +269,20 @@ object MLTestingUtils extends SparkFunSuite {
assert(newDatasetF.schema(featuresColName).dataType.equals(new
ArrayType(FloatType, false)))
(newDataset, newDatasetD, newDatasetF)
}
+
+ def modelPredictionEquals[M <: PredictionModel[_, M]](
Review comment:
yes, I already made the tolerance looser in latest commit for the regressor
(maybe you are not looking at latest code?). Note for classifier there is no
tolerance. See update:
```
MLTestingUtils.testArbitrarilyScaledWeights[DecisionTreeRegressionModel,
DecisionTreeRegressor](df.as[LabeledPoint], estimator,
MLTestingUtils.modelPredictionEquals(df, _ ~= _ relTol 0.1, 0.99))
MLTestingUtils.testOutliersWithSmallWeights[DecisionTreeRegressionModel,
DecisionTreeRegressor](df.as[LabeledPoint], estimator, numClasses,
MLTestingUtils.modelPredictionEquals(df, _ ~= _ relTol 0.1, 0.99),
outlierRatio = 2)
MLTestingUtils.testOversamplingVsWeighting[DecisionTreeRegressionModel,
DecisionTreeRegressor](df.as[LabeledPoint], estimator,
MLTestingUtils.modelPredictionEquals(df, _ ~= _ relTol 0.01, 1.0),
seed)
```
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