Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3374#discussion_r20629859
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala ---
@@ -40,151 +39,98 @@ import org.apache.spark.storage.StorageLevel
* Notes:
* - This currently can be run with several loss functions. However,
only SquaredError is
* fully supported. Specifically, the loss function should be used to
compute the gradient
- * (to re-label training instances on each iteration) and to weight
weak hypotheses.
+ * (to re-label training instances on each iteration) and to weight
tree ensembles.
* Currently, gradients are computed correctly for the available loss
functions,
- * but weak hypothesis weights are not computed correctly for LogLoss
or AbsoluteError.
- * Running with those losses will likely behave reasonably, but lacks
the same guarantees.
+ * but tree predictions are not computed correctly for LogLoss or
AbsoluteError since they
+ * use the mean of the samples at each leaf node. Running with those
losses will likely behave
+ * reasonably, but lacks the same guarantees.
*
- * @param boostingStrategy Parameters for the gradient boosting algorithm
+ * @param boostingStrategy Parameters for the gradient boosting algorithm.
*/
@Experimental
-class GradientBoosting (
- private val boostingStrategy: BoostingStrategy) extends Serializable
with Logging {
-
- boostingStrategy.weakLearnerParams.algo = Regression
- boostingStrategy.weakLearnerParams.impurity = impurity.Variance
-
- // Ensure values for weak learner are the same as what is provided to
the boosting algorithm.
- boostingStrategy.weakLearnerParams.numClassesForClassification =
- boostingStrategy.numClassesForClassification
-
- boostingStrategy.assertValid()
+class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
+ extends Serializable with Logging {
/**
* Method to train a gradient boosting model
* @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
- * @return WeightedEnsembleModel that can be used for prediction
+ * @return a gradient boosted trees model that can be used for prediction
*/
- def train(input: RDD[LabeledPoint]): WeightedEnsembleModel = {
- val algo = boostingStrategy.algo
+ def run(input: RDD[LabeledPoint]): GradientBoostedTreesModel = {
+ val algo = boostingStrategy.treeStrategy.algo
algo match {
- case Regression => GradientBoosting.boost(input, boostingStrategy)
+ case Regression => GradientBoostedTrees.boost(input,
boostingStrategy)
case Classification =>
// Map labels to -1, +1 so binary classification can be treated as
regression.
val remappedInput = input.map(x => new LabeledPoint((x.label * 2)
- 1, x.features))
- GradientBoosting.boost(remappedInput, boostingStrategy)
+ GradientBoostedTrees.boost(remappedInput, boostingStrategy)
case _ =>
throw new IllegalArgumentException(s"$algo is not supported by the
gradient boosting.")
}
}
+ /**
+ * Java-friendly API for
[[org.apache.spark.mllib.tree.GradientBoostedTrees!#run]].
+ */
+ def run(input: JavaRDD[LabeledPoint]): GradientBoostedTreesModel = {
+ run(input.rdd)
+ }
}
-object GradientBoosting extends Logging {
+object GradientBoostedTrees extends Logging {
/**
* Method to train a gradient boosting model.
*
- * Note: Using
[[org.apache.spark.mllib.tree.GradientBoosting$#trainRegressor]]
- * is recommended to clearly specify regression.
- * Using
[[org.apache.spark.mllib.tree.GradientBoosting$#trainClassifier]]
- * is recommended to clearly specify regression.
- *
* @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
* For classification, labels should take values {0, 1,
..., numClasses-1}.
* For regression, labels are real numbers.
* @param boostingStrategy Configuration options for the boosting
algorithm.
- * @return WeightedEnsembleModel that can be used for prediction
+ * @return a gradient boosted trees model that can be used for prediction
*/
def train(
input: RDD[LabeledPoint],
- boostingStrategy: BoostingStrategy): WeightedEnsembleModel = {
- new GradientBoosting(boostingStrategy).train(input)
+ boostingStrategy: BoostingStrategy): GradientBoostedTreesModel = {
+ new GradientBoostedTrees(boostingStrategy).run(input)
}
/**
- * Method to train a gradient boosting classification model.
- *
- * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
- * For classification, labels should take values {0, 1,
..., numClasses-1}.
- * For regression, labels are real numbers.
- * @param boostingStrategy Configuration options for the boosting
algorithm.
- * @return WeightedEnsembleModel that can be used for prediction
- */
- def trainClassifier(
- input: RDD[LabeledPoint],
- boostingStrategy: BoostingStrategy): WeightedEnsembleModel = {
- val algo = boostingStrategy.algo
- require(algo == Classification, s"Only Classification algo supported.
Provided algo is $algo.")
- new GradientBoosting(boostingStrategy).train(input)
- }
-
- /**
- * Method to train a gradient boosting regression model.
- *
- * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
- * For classification, labels should take values {0, 1,
..., numClasses-1}.
- * For regression, labels are real numbers.
- * @param boostingStrategy Configuration options for the boosting
algorithm.
- * @return WeightedEnsembleModel that can be used for prediction
- */
- def trainRegressor(
- input: RDD[LabeledPoint],
- boostingStrategy: BoostingStrategy): WeightedEnsembleModel = {
- val algo = boostingStrategy.algo
- require(algo == Regression, s"Only Regression algo supported. Provided
algo is $algo.")
- new GradientBoosting(boostingStrategy).train(input)
- }
-
- /**
- * Java-friendly API for
[[org.apache.spark.mllib.tree.GradientBoosting$#train]]
+ * Java-friendly API for
[[org.apache.spark.mllib.tree.GradientBoostedTrees$#train]]
*/
def train(
- input: JavaRDD[LabeledPoint],
- boostingStrategy: BoostingStrategy): WeightedEnsembleModel = {
- train(input.rdd, boostingStrategy)
- }
-
- /**
- * Java-friendly API for
[[org.apache.spark.mllib.tree.GradientBoosting$#trainClassifier]]
- */
- def trainClassifier(
- input: JavaRDD[LabeledPoint],
- boostingStrategy: BoostingStrategy): WeightedEnsembleModel = {
- trainClassifier(input.rdd, boostingStrategy)
- }
-
- /**
- * Java-friendly API for
[[org.apache.spark.mllib.tree.GradientBoosting$#trainRegressor]]
- */
- def trainRegressor(
input: JavaRDD[LabeledPoint],
- boostingStrategy: BoostingStrategy): WeightedEnsembleModel = {
- trainRegressor(input.rdd, boostingStrategy)
+ boostingStrategy: BoostingStrategy): GradientBoostedTreesModel = {
+ train(input.rdd, boostingStrategy)
}
/**
* Internal method for performing regression using trees as base
learners.
* @param input training dataset
* @param boostingStrategy boosting parameters
- * @return
+ * @return a gradient boosted trees model that can be used for prediction
*/
private def boost(
input: RDD[LabeledPoint],
- boostingStrategy: BoostingStrategy): WeightedEnsembleModel = {
+ boostingStrategy: BoostingStrategy): GradientBoostedTreesModel = {
val timer = new TimeTracker()
timer.start("total")
timer.start("init")
+ boostingStrategy.assertValid()
+
// Initialize gradient boosting parameters
val numIterations = boostingStrategy.numIterations
val baseLearners = new Array[DecisionTreeModel](numIterations)
val baseLearnerWeights = new Array[Double](numIterations)
val loss = boostingStrategy.loss
val learningRate = boostingStrategy.learningRate
- val strategy = boostingStrategy.weakLearnerParams
+ // Prepare strategy for tree ensembles. Tree ensembles use regression
with variance impurity.
--- End diff --
done
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