Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/2607#discussion_r19496266
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/model/WeightedEnsembleModel.scala
---
@@ -0,0 +1,177 @@
+/*
+ * 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.mllib.tree.model
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.tree.configuration.Algo._
+import
org.apache.spark.mllib.tree.configuration.EnsembleCombiningStrategy._
+import org.apache.spark.rdd.RDD
+
+import scala.collection.mutable
+
+@Experimental
+class WeightedEnsembleModel(
+ val baseLearners: Array[DecisionTreeModel],
+ val baseLearnerWeights: Array[Double],
+ val algo: Algo,
+ val combiningStrategy: EnsembleCombiningStrategy) extends Serializable
{
+
+ require(numTrees > 0, s"WeightedEnsembleModel cannot be created without
base learners. Number " +
+ s"of baselearners = $baseLearners")
+
+ /**
+ * Predict values for a single data point using the model trained.
+ *
+ * @param features array representing a single data point
+ * @return predicted category from the trained model
+ */
+ private def predictRaw(features: Vector): Double = {
+ val treePredictions = baseLearners.map(learner =>
learner.predict(features))
+ if (numTrees == 1){
+ treePredictions(0)
+ } else {
+ var prediction = treePredictions(0)
+ var index = 1
+ while (index < numTrees) {
+ prediction += baseLearnerWeights(index) * treePredictions(index)
+ index += 1
+ }
+ prediction
+ }
+ }
+
+ /**
+ * Predict values for a single data point using the model trained.
+ *
+ * @param features array representing a single data point
+ * @return predicted category from the trained model
+ */
+ private def predictBySumming(features: Vector): Double = {
+ val treePredictions = baseLearners.map(learner =>
learner.predict(features))
+ val rawPrediction = {
+ if (numTrees == 1) {
+ treePredictions(0)
+ } else {
+ var prediction = treePredictions(0)
+ var index = 1
+ while (index < numTrees) {
+ prediction += baseLearnerWeights(index) * treePredictions(index)
+ index += 1
+ }
+ prediction
+ }
+ }
+ algo match {
+ case Regression => predictRaw(features)
+ case Classification => {
+ // TODO: predicted labels are +1 or -1 for GBT. Need a better way
to store this info.
+ if (predictRaw(features) > 0 ) 1.0 else 0.0
+ }
+ case _ => throw new IllegalArgumentException(
+ s"WeightedEnsembleModel given unknown algo parameter: $algo.")
+ }
+ }
+
+ /**
+ * Predict values for a single data point.
+ *
+ * @param features array representing a single data point
+ * @return Double prediction from the trained model
+ */
+ def predictByAveraging(features: Vector): Double = {
+ algo match {
+ case Classification =>
+ val predictionToCount = new mutable.HashMap[Int, Int]()
+ baseLearners.foreach { learner =>
+ val prediction = learner.predict(features).toInt
+ predictionToCount(prediction) =
predictionToCount.getOrElse(prediction, 0) + 1
+ }
+ predictionToCount.maxBy(_._2)._1
+ case Regression =>
+ baseLearners.map(_.predict(features)).sum / baseLearners.size
+ }
+ }
+
+
+ /**
+ * Predict values for a single data point using the model trained.
+ *
+ * @param features array representing a single data point
+ * @return predicted category from the trained model
+ */
+ def predict(features: Vector): Double = {
+ combiningStrategy match {
+ case Sum => predictBySumming(features)
+ case Average => predictByAveraging(features)
+ case _ => throw new IllegalArgumentException(
+ s"WeightedEnsembleModel given unknown combining parameter:
$combiningStrategy.")
+ }
+ }
+
+ /**
+ * Predict values for the given data set.
+ *
+ * @param features RDD representing data points to be predicted
+ * @return RDD[Double] where each entry contains the corresponding
prediction
+ */
+ def predict(features: RDD[Vector]): RDD[Double] = features.map(x =>
predict(x))
+
+ /**
+ * Print full model.
+ */
+ override def toString: String = {
+ val header = algo match {
+ case Classification =>
+ s"WeightedEnsembleModel classifier with $numTrees trees\n"
+ case Regression =>
+ s"WeightedEnsembleModel regressor with $numTrees trees\n"
+ case _ => throw new IllegalArgumentException(
+ s"WeightedEnsembleModel given unknown algo parameter: $algo.")
+ }
+ header + baseLearners.zipWithIndex.map { case (learner, treeIndex) =>
+ s" Tree $treeIndex:\n" + learner.topNode.subtreeToString(4)
+ }.fold("")(_ + _)
+ }
+
+ /**
+ * Print the full model to a string.
+ */
+ def toDebugString: String = {
+ val header = toString + "\n"
+ header + baseLearners.zipWithIndex.map { case (tree, treeIndex) =>
+ s" Tree $treeIndex:\n" + tree.topNode.subtreeToString(4)
+ }.fold("")(_ + _)
+ }
+
+
+ // TODO: Remove these helpers methods once class is generalized to
support any base learning
+ // algorithms.
+
+ /**
+ * Get number of trees in forest.
+ */
+ def numTrees: Int = baseLearners.size
--- End diff --
Agree.
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