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author | MechCoder <manojkumarsivaraj334@gmail.com> | 2015-02-24 15:13:22 -0800 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-02-24 15:13:22 -0800 |
commit | 2a0fe34891882e0fde1b5722d8227aa99acc0f1f (patch) | |
tree | 238c58f540e0b8c727e131b6359041d137c4e780 /mllib/src/test | |
parent | da505e59274d1c838653c1109db65ad374e65304 (diff) | |
download | spark-2a0fe34891882e0fde1b5722d8227aa99acc0f1f.tar.gz spark-2a0fe34891882e0fde1b5722d8227aa99acc0f1f.tar.bz2 spark-2a0fe34891882e0fde1b5722d8227aa99acc0f1f.zip |
[SPARK-5436] [MLlib] Validate GradientBoostedTrees using runWithValidation
One can early stop if the decrease in error rate is lesser than a certain tol or if the error increases if the training data is overfit.
This introduces a new method runWithValidation which takes in a pair of RDD's , one for the training data and the other for the validation.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes #4677 from MechCoder/spark-5436 and squashes the following commits:
1bb21d4 [MechCoder] Combine regression and classification tests into a single one
e4d799b [MechCoder] Addresses indentation and doc comments
b48a70f [MechCoder] COSMIT
b928a19 [MechCoder] Move validation while training section under usage tips
fad9b6e [MechCoder] Made the following changes 1. Add section to documentation 2. Return corresponding to bestValidationError 3. Allow negative tolerance.
55e5c3b [MechCoder] One liner for prevValidateError
3e74372 [MechCoder] TST: Add test for classification
77549a9 [MechCoder] [SPARK-5436] Validate GradientBoostedTrees using runWithValidation
Diffstat (limited to 'mllib/src/test')
-rw-r--r-- | mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala | 36 |
1 files changed, 36 insertions, 0 deletions
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala index bde47606eb..b437aeaaf0 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala @@ -158,6 +158,40 @@ class GradientBoostedTreesSuite extends FunSuite with MLlibTestSparkContext { } } } + + test("runWithValidation stops early and performs better on a validation dataset") { + // Set numIterations large enough so that it stops early. + val numIterations = 20 + val trainRdd = sc.parallelize(GradientBoostedTreesSuite.trainData, 2) + val validateRdd = sc.parallelize(GradientBoostedTreesSuite.validateData, 2) + + val algos = Array(Regression, Regression, Classification) + val losses = Array(SquaredError, AbsoluteError, LogLoss) + (algos zip losses) map { + case (algo, loss) => { + val treeStrategy = new Strategy(algo = algo, impurity = Variance, maxDepth = 2, + categoricalFeaturesInfo = Map.empty) + val boostingStrategy = + new BoostingStrategy(treeStrategy, loss, numIterations, validationTol = 0.0) + val gbtValidate = new GradientBoostedTrees(boostingStrategy) + .runWithValidation(trainRdd, validateRdd) + assert(gbtValidate.numTrees !== numIterations) + + // Test that it performs better on the validation dataset. + val gbt = GradientBoostedTrees.train(trainRdd, boostingStrategy) + val (errorWithoutValidation, errorWithValidation) = { + if (algo == Classification) { + val remappedRdd = validateRdd.map(x => new LabeledPoint(2 * x.label - 1, x.features)) + (loss.computeError(gbt, remappedRdd), loss.computeError(gbtValidate, remappedRdd)) + } else { + (loss.computeError(gbt, validateRdd), loss.computeError(gbtValidate, validateRdd)) + } + } + assert(errorWithValidation <= errorWithoutValidation) + } + } + } + } private object GradientBoostedTreesSuite { @@ -166,4 +200,6 @@ private object GradientBoostedTreesSuite { val testCombinations = Array((10, 1.0, 1.0), (10, 0.1, 1.0), (10, 0.5, 0.75), (10, 0.1, 0.75)) val data = EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 10, 100) + val trainData = EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 20, 120) + val validateData = EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 20, 80) } |