Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/3374#discussion_r20621257
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala ---
@@ -45,146 +43,92 @@ import org.apache.spark.storage.StorageLevel
* 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.
*
- * @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
--- End diff --
Very minor nit: A gradient boosted trees model that can be used for
prediction.
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