[GitHub] spark pull request #21129: [SPARK-7132][ML] Add fit with validation set to s...

[jkbradley <gi...@git.apache.org>]

Github user jkbradley commented on a diff in the pull request:

    https://github.com/apache/spark/pull/21129#discussion_r186572374
  
    --- Diff: mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala ---
    @@ -365,6 +366,50 @@ class GBTClassifierSuite extends MLTest with DefaultReadWriteTest {
         assert(mostImportantFeature !== mostIF)
       }
     
    +  test("runWithValidation stops early and performs better on a validation dataset") {
    +    val validationIndicatorCol = "validationIndicator"
    +    val trainDF = trainData.toDF().withColumn(validationIndicatorCol, lit(false))
    +    val validationDF = validationData.toDF().withColumn(validationIndicatorCol, lit(true))
    +
    +    val numIter = 20
    +    for (lossType <- GBTClassifier.supportedLossTypes) {
    +      val gbt = new GBTClassifier()
    +        .setSeed(123)
    +        .setMaxDepth(2)
    +        .setLossType(lossType)
    +        .setMaxIter(numIter)
    +      val modelWithoutValidation = gbt.fit(trainDF)
    +
    +      gbt.setValidationIndicatorCol(validationIndicatorCol)
    +      val modelWithValidation = gbt.fit(trainDF.union(validationDF))
    +
    +      // early stop
    +      assert(modelWithValidation.numTrees < numIter)
    +
    +      val (errorWithoutValidation, errorWithValidation) = {
    +        val remappedRdd = validationData.map(x => new LabeledPoint(2 * x.label - 1, x.features))
    +        (GradientBoostedTrees.computeError(remappedRdd, modelWithoutValidation.trees,
    +          modelWithoutValidation.treeWeights, modelWithoutValidation.getOldLossType),
    +          GradientBoostedTrees.computeError(remappedRdd, modelWithValidation.trees,
    +            modelWithValidation.treeWeights, modelWithValidation.getOldLossType))
    +      }
    +      assert(errorWithValidation <= errorWithoutValidation)
    --- End diff --
    
    It'd be nice to have this be strictly true. Is it not?


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