Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3374#discussion_r20629860
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
---
@@ -0,0 +1,178 @@
+/*
+ * 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 scala.collection.mutable
+
+import com.github.fommil.netlib.BLAS.{getInstance => blas}
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaRDD
+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
+
+/**
+ * :: Experimental ::
+ * Represents a random forest model.
+ *
+ * @param algo algorithm for the ensemble model, either Classification or
Regression
+ * @param trees tree ensembles
+ */
+@Experimental
+class RandomForestModel(override val algo: Algo, override val trees:
Array[DecisionTreeModel])
+ extends TreeEnsembleModel(algo, trees, Array.fill(trees.size)(1.0),
+ combiningStrategy = if (algo == Classification) Vote else Average) {
+
+ require(trees.forall(_.algo == algo))
+}
+
+/**
+ * :: Experimental ::
+ * Represents a gradient boosted trees model.
+ *
+ * @param algo algorithm for the ensemble model, either Classification or
Regression
+ * @param trees tree ensembles
+ * @param treeWeights tree ensemble weights
+ */
+@Experimental
+class GradientBoostedTreesModel(
+ override val algo: Algo,
+ override val trees: Array[DecisionTreeModel],
+ override val treeWeights: Array[Double])
+ extends TreeEnsembleModel(algo, trees, treeWeights, combiningStrategy =
Sum) {
+
+ require(trees.size == treeWeights.size)
+}
+
+/**
+ * Represents a tree ensemble model.
+ *
+ * @param algo algorithm for the ensemble model, either Classification or
Regression
+ * @param trees tree ensembles
+ * @param treeWeights tree ensemble weights
+ * @param combiningStrategy strategy for combining the predictions, not
used for regression.
+ */
+private[tree] sealed class TreeEnsembleModel(
+ protected val algo: Algo,
+ protected val trees: Array[DecisionTreeModel],
+ protected val treeWeights: Array[Double],
+ protected val combiningStrategy: EnsembleCombiningStrategy) extends
Serializable {
+
+ require(numTrees > 0, "TreeEnsembleModel cannot be created without
trees.")
+
+ private val sumWeights = math.max(treeWeights.sum, 1e-15)
+
+ /**
+ * Predicts for a single data point using the weighted sum of ensemble
predictions.
+ *
+ * @param features array representing a single data point
+ * @return predicted category from the trained model
+ */
+ private def predictBySumming(features: Vector): Double = {
+ val treePredictions = trees.map(learner => learner.predict(features))
--- End diff --
done
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