Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5669#discussion_r29004162
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala ---
@@ -177,102 +177,108 @@ object GradientBoostedTrees extends Logging {
treeStrategy.assertValid()
// Cache input
- if (input.getStorageLevel == StorageLevel.NONE) {
+ val persistedInput = if (input.getStorageLevel == StorageLevel.NONE) {
input.persist(StorageLevel.MEMORY_AND_DISK)
- }
+ true
+ } else false
- timer.stop("init")
+ try {
+ timer.stop("init")
- logDebug("##########")
- logDebug("Building tree 0")
- logDebug("##########")
- var data = input
+ logDebug("##########")
+ logDebug("Building tree 0")
+ logDebug("##########")
+ var data = input
- // Initialize tree
- timer.start("building tree 0")
- val firstTreeModel = new DecisionTree(treeStrategy).run(data)
- val firstTreeWeight = 1.0
- baseLearners(0) = firstTreeModel
- baseLearnerWeights(0) = firstTreeWeight
+ // Initialize tree
+ timer.start("building tree 0")
+ val firstTreeModel = new DecisionTree(treeStrategy).run(data)
+ val firstTreeWeight = 1.0
+ baseLearners(0) = firstTreeModel
+ baseLearnerWeights(0) = firstTreeWeight
- var predError: RDD[(Double, Double)] = GradientBoostedTreesModel.
- computeInitialPredictionAndError(input, firstTreeWeight,
firstTreeModel, loss)
- logDebug("error of gbt = " + predError.values.mean())
+ var predError: RDD[(Double, Double)] = GradientBoostedTreesModel.
+ computeInitialPredictionAndError(input, firstTreeWeight,
firstTreeModel, loss)
+ logDebug("error of gbt = " + predError.values.mean())
- // Note: A model of type regression is used since we require raw
prediction
- timer.stop("building tree 0")
+ // Note: A model of type regression is used since we require raw
prediction
+ timer.stop("building tree 0")
- var validatePredError: RDD[(Double, Double)] =
GradientBoostedTreesModel.
- computeInitialPredictionAndError(validationInput, firstTreeWeight,
firstTreeModel, loss)
- var bestValidateError = if (validate) validatePredError.values.mean()
else 0.0
- var bestM = 1
+ var validatePredError: RDD[(Double, Double)] =
GradientBoostedTreesModel.
+ computeInitialPredictionAndError(validationInput, firstTreeWeight,
firstTreeModel, loss)
+ var bestValidateError = if (validate)
validatePredError.values.mean() else 0.0
+ var bestM = 1
- // pseudo-residual for second iteration
- data = predError.zip(input).map { case ((pred, _), point) =>
- LabeledPoint(-loss.gradient(pred, point.label), point.features)
- }
+ // pseudo-residual for second iteration
+ data = predError.zip(input).map { case ((pred, _), point) =>
+ LabeledPoint(-loss.gradient(pred, point.label), point.features)
+ }
- var m = 1
- while (m < numIterations) {
- timer.start(s"building tree $m")
- logDebug("###################################################")
- logDebug("Gradient boosting tree iteration " + m)
- logDebug("###################################################")
- val model = new DecisionTree(treeStrategy).run(data)
- timer.stop(s"building tree $m")
- // Create partial model
- baseLearners(m) = model
- // Note: The setting of baseLearnerWeights is incorrect for losses
other than SquaredError.
- // Technically, the weight should be optimized for the
particular loss.
- // However, the behavior should be reasonable, though not
optimal.
- baseLearnerWeights(m) = learningRate
- // Note: A model of type regression is used since we require raw
prediction
- val partialModel = new GradientBoostedTreesModel(
- Regression, baseLearners.slice(0, m + 1),
- baseLearnerWeights.slice(0, m + 1))
+ var m = 1
+ while (m < numIterations) {
+ timer.start(s"building tree $m")
+ logDebug("###################################################")
+ logDebug("Gradient boosting tree iteration " + m)
+ logDebug("###################################################")
+ val model = new DecisionTree(treeStrategy).run(data)
+ timer.stop(s"building tree $m")
+ // Create partial model
+ baseLearners(m) = model
+ // Note: The setting of baseLearnerWeights is incorrect for losses
other than SquaredError.
+ // Technically, the weight should be optimized for the
particular loss.
+ // However, the behavior should be reasonable, though not
optimal.
+ baseLearnerWeights(m) = learningRate
+ // Note: A model of type regression is used since we require raw
prediction
+ val partialModel = new GradientBoostedTreesModel(
+ Regression, baseLearners.slice(0, m + 1),
+ baseLearnerWeights.slice(0, m + 1))
- predError = GradientBoostedTreesModel.updatePredictionError(
- input, predError, baseLearnerWeights(m), baseLearners(m), loss)
- logDebug("error of gbt = " + predError.values.mean())
+ predError = GradientBoostedTreesModel.updatePredictionError(
+ input, predError, baseLearnerWeights(m), baseLearners(m), loss)
+ logDebug("error of gbt = " + predError.values.mean())
- if (validate) {
- // Stop training early if
- // 1. Reduction in error is less than the validationTol or
- // 2. If the error increases, that is if the model is overfit.
- // We want the model returned corresponding to the best validation
error.
+ if (validate) {
+ // Stop training early if
+ // 1. Reduction in error is less than the validationTol or
+ // 2. If the error increases, that is if the model is overfit.
+ // We want the model returned corresponding to the best
validation error.
- validatePredError =
GradientBoostedTreesModel.updatePredictionError(
- validationInput, validatePredError, baseLearnerWeights(m),
baseLearners(m), loss)
- val currentValidateError = validatePredError.values.mean()
- if (bestValidateError - currentValidateError < validationTol) {
- return new GradientBoostedTreesModel(
- boostingStrategy.treeStrategy.algo,
- baseLearners.slice(0, bestM),
- baseLearnerWeights.slice(0, bestM))
- } else if (currentValidateError < bestValidateError) {
- bestValidateError = currentValidateError
- bestM = m + 1
+ validatePredError =
GradientBoostedTreesModel.updatePredictionError(
+ validationInput, validatePredError, baseLearnerWeights(m),
baseLearners(m), loss)
+ val currentValidateError = validatePredError.values.mean()
+ if (bestValidateError - currentValidateError < validationTol) {
+ return new GradientBoostedTreesModel(
+ boostingStrategy.treeStrategy.algo,
+ baseLearners.slice(0, bestM),
+ baseLearnerWeights.slice(0, bestM))
+ } else if (currentValidateError < bestValidateError) {
+ bestValidateError = currentValidateError
+ bestM = m + 1
+ }
}
+ // Update data with pseudo-residuals
+ data = predError.zip(input).map { case ((pred, _), point) =>
+ LabeledPoint(-loss.gradient(pred, point.label), point.features)
+ }
+ m += 1
}
- // Update data with pseudo-residuals
- data = predError.zip(input).map { case ((pred, _), point) =>
- LabeledPoint(-loss.gradient(pred, point.label), point.features)
- }
- m += 1
- }
- timer.stop("total")
+ timer.stop("total")
+
+ logInfo("Internal timing for DecisionTree:")
+ logInfo(s"$timer")
+ if (validate) {
+ new GradientBoostedTreesModel(
+ boostingStrategy.treeStrategy.algo,
+ baseLearners.slice(0, bestM),
+ baseLearnerWeights.slice(0, bestM))
+ } else {
+ new GradientBoostedTreesModel(
+ boostingStrategy.treeStrategy.algo, baseLearners,
baseLearnerWeights)
+ }
- logInfo("Internal timing for DecisionTree:")
- logInfo(s"$timer")
- if (validate) {
- new GradientBoostedTreesModel(
- boostingStrategy.treeStrategy.algo,
- baseLearners.slice(0, bestM),
- baseLearnerWeights.slice(0, bestM))
- } else {
- new GradientBoostedTreesModel(
- boostingStrategy.treeStrategy.algo, baseLearners,
baseLearnerWeights)
+ } finally {
+ if (persistedInput) input.unpersist()
--- End diff --
I agree it's cleaner this way, if a little more complex. Concerning
consistency, let me ping @mengxr since, if we do this here, we might start
talking about doing it everywhere.
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