Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5626#discussion_r29024771
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
---
@@ -0,0 +1,167 @@
+/*
+ * 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.regression
+
+import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
+import org.apache.spark.ml.impl.tree.{RandomForestParams,
TreeRegressorParams}
+import org.apache.spark.ml.param.{Params, ParamMap}
+import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel}
+import org.apache.spark.ml.util.MetadataUtils
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest}
+import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo,
Strategy => OldStrategy}
+import org.apache.spark.mllib.tree.model.{RandomForestModel =>
OldRandomForestModel}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.DataFrame
+
+
+/**
+ * :: AlphaComponent ::
+ *
+ * [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] learning
algorithm for regression.
+ * It supports both continuous and categorical features.
+ */
+@AlphaComponent
+final class RandomForestRegressor
+ extends Predictor[Vector, RandomForestRegressor,
RandomForestRegressionModel]
+ with RandomForestParams with TreeRegressorParams {
+
+ // Override parameter setters from parent trait for Java API
compatibility.
+
+ // Parameters from TreeRegressorParams:
+
+ override def setMaxDepth(value: Int): this.type =
super.setMaxDepth(value)
+
+ override def setMaxBins(value: Int): this.type = super.setMaxBins(value)
+
+ override def setMinInstancesPerNode(value: Int): this.type =
+ super.setMinInstancesPerNode(value)
+
+ override def setMinInfoGain(value: Double): this.type =
super.setMinInfoGain(value)
+
+ override def setMaxMemoryInMB(value: Int): this.type =
super.setMaxMemoryInMB(value)
+
+ override def setCacheNodeIds(value: Boolean): this.type =
super.setCacheNodeIds(value)
+
+ override def setCheckpointInterval(value: Int): this.type =
super.setCheckpointInterval(value)
+
+ override def setImpurity(value: String): this.type =
super.setImpurity(value)
+
+ // Parameters from TreeEnsembleParams:
+
+ override def setSubsamplingRate(value: Double): this.type =
super.setSubsamplingRate(value)
+
+ override def setSeed(value: Long): this.type = super.setSeed(value)
+
+ // Parameters from RandomForestParams:
+
+ override def setNumTrees(value: Int): this.type =
super.setNumTrees(value)
+
+ override def setFeatureSubsetStrategy(value: String): this.type =
+ super.setFeatureSubsetStrategy(value)
+
+ override protected def train(
+ dataset: DataFrame,
+ paramMap: ParamMap): RandomForestRegressionModel = {
+ val categoricalFeatures: Map[Int, Int] =
+
MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol)))
+ val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset,
paramMap)
+ val strategy =
+ super.getOldStrategy(categoricalFeatures, numClasses = 0,
OldAlgo.Regression, getOldImpurity)
+ val oldModel = OldRandomForest.trainRegressor(
+ oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy,
getSeed.toInt)
+ RandomForestRegressionModel.fromOld(oldModel, this, paramMap,
categoricalFeatures)
+ }
+}
+
+object RandomForestRegressor {
+ /** Accessor for supported impurity settings: variance */
+ final val supportedImpurities: Array[String] =
TreeRegressorParams.supportedImpurities
+
+ /** Accessor for supported featureSubsetStrategy settings: auto, all,
onethird, sqrt, log2 */
+ final val supportedFeatureSubsetStrategies: Array[String] =
+ RandomForestParams.supportedFeatureSubsetStrategies
+}
+
+/**
+ * :: AlphaComponent ::
+ *
+ * [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] model for
regression.
+ * It supports both continuous and categorical features.
+ * @param _trees Decision trees in the ensemble.
+ */
+@AlphaComponent
+final class RandomForestRegressionModel private[ml] (
+ override val parent: RandomForestRegressor,
+ override val fittingParamMap: ParamMap,
+ private val _trees: Array[DecisionTreeRegressionModel])
+ extends PredictionModel[Vector, RandomForestRegressionModel]
+ with TreeEnsembleModel with Serializable {
+
+ require(numTrees > 0, "RandomForestRegressionModel requires at least 1
tree.")
+
+ override def trees: Array[DecisionTreeModel] =
_trees.asInstanceOf[Array[DecisionTreeModel]]
+
+ // Note: We may add support for weights (based on tree performance)
later on.
+ lazy val _treeWeights: Array[Double] = Array.fill[Double](numTrees)(1.0)
--- End diff --
private
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