Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/10607#discussion_r48907328
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tree/impl/GradientBoostedTrees.scala
---
@@ -0,0 +1,272 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tree.impl
+
+import org.apache.spark.Logging
+import org.apache.spark.mllib.impl.PeriodicRDDCheckpointer
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.ml.regression.{DecisionTreeRegressionModel,
DecisionTreeRegressor}
+import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.mllib.tree.configuration.BoostingStrategy
+import org.apache.spark.mllib.tree.impl.TimeTracker
+import org.apache.spark.mllib.tree.impurity.Variance
+import org.apache.spark.mllib.tree.loss.Loss
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+
+private[ml] object GradientBoostedTrees extends Logging {
+
+ /**
+ * Method to train a gradient boosting model
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * @return a gradient boosted trees model that can be used for prediction
+ */
+ def run(input: RDD[LabeledPoint],
+ boostingStrategy: BoostingStrategy):
(Array[DecisionTreeRegressionModel], Array[Double]) = {
+ val algo = boostingStrategy.treeStrategy.algo
+ algo match {
+ case Regression =>
+ GradientBoostedTrees.boost(input, input, boostingStrategy,
validate = false)
+ 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))
+ GradientBoostedTrees.boost(remappedInput, remappedInput,
boostingStrategy, validate = false)
+ case _ =>
+ throw new IllegalArgumentException(s"$algo is not supported by
gradient boosting.")
+ }
+ }
+
+ /**
+ * Method to validate a gradient boosting model
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * @param validationInput Validation dataset.
+ * This dataset should be different from the
training dataset,
+ * but it should follow the same distribution.
+ * E.g., these two datasets could be created from
an original dataset
+ * by using
[[org.apache.spark.rdd.RDD.randomSplit()]]
+ * @return a gradient boosted trees model that can be used for prediction
+ */
+ def runWithValidation(
+ input: RDD[LabeledPoint],
+ validationInput: RDD[LabeledPoint],
+ boostingStrategy: BoostingStrategy):
(Array[DecisionTreeRegressionModel], Array[Double]) = {
+ val algo = boostingStrategy.treeStrategy.algo
+ algo match {
+ case Regression =>
+ GradientBoostedTrees.boost(input, validationInput,
boostingStrategy, validate = true)
+ case Classification =>
+ // Map labels to -1, +1 so binary classification can be treated as
regression.
+ val remappedInput = input.map(
+ x => new LabeledPoint((x.label * 2) - 1, x.features))
+ val remappedValidationInput = validationInput.map(
+ x => new LabeledPoint((x.label * 2) - 1, x.features))
+ GradientBoostedTrees.boost(remappedInput, remappedValidationInput,
boostingStrategy,
+ validate = true)
+ case _ =>
+ throw new IllegalArgumentException(s"$algo is not supported by the
gradient boosting.")
+ }
+ }
+
+ /**
+ * Compute the initial predictions and errors for a dataset for the first
+ * iteration of gradient boosting.
+ * @param data: training data.
+ * @param initTreeWeight: learning rate assigned to the first tree.
+ * @param initTree: first DecisionTreeModel.
+ * @param loss: evaluation metric.
+ * @return a RDD with each element being a zip of the prediction and
error
+ * corresponding to every sample.
+ */
+ def computeInitialPredictionAndError(
--- End diff --
`computeInitialPredictionAndError` and `updatePredictionError` used to be
part of the developer api but are private[ml] in this implementation. I am not
sure if it would be a problem to make these private[ml].
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