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/*
* 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.{Experimental, Since}
import org.apache.spark.ml.{PredictionModel, Predictor}
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeEnsembleModel, TreeRegressorParams}
import org.apache.spark.ml.tree.impl.RandomForest
import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions._
/**
* :: Experimental ::
* [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] learning algorithm for regression.
* It supports both continuous and categorical features.
*/
@Since("1.4.0")
@Experimental
final class RandomForestRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
extends Predictor[Vector, RandomForestRegressor, RandomForestRegressionModel]
with RandomForestParams with TreeRegressorParams {
@Since("1.4.0")
def this() = this(Identifiable.randomUID("rfr"))
// Override parameter setters from parent trait for Java API compatibility.
// Parameters from TreeRegressorParams:
@Since("1.4.0")
override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value)
@Since("1.4.0")
override def setMaxBins(value: Int): this.type = super.setMaxBins(value)
@Since("1.4.0")
override def setMinInstancesPerNode(value: Int): this.type =
super.setMinInstancesPerNode(value)
@Since("1.4.0")
override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value)
@Since("1.4.0")
override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value)
@Since("1.4.0")
override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value)
@Since("1.4.0")
override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value)
@Since("1.4.0")
override def setImpurity(value: String): this.type = super.setImpurity(value)
// Parameters from TreeEnsembleParams:
@Since("1.4.0")
override def setSubsamplingRate(value: Double): this.type = super.setSubsamplingRate(value)
@Since("1.4.0")
override def setSeed(value: Long): this.type = super.setSeed(value)
// Parameters from RandomForestParams:
@Since("1.4.0")
override def setNumTrees(value: Int): this.type = super.setNumTrees(value)
@Since("1.4.0")
override def setFeatureSubsetStrategy(value: String): this.type =
super.setFeatureSubsetStrategy(value)
override protected def train(dataset: DataFrame): RandomForestRegressionModel = {
val categoricalFeatures: Map[Int, Int] =
MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
val strategy =
super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity)
val trees =
RandomForest.run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed)
.map(_.asInstanceOf[DecisionTreeRegressionModel])
val numFeatures = oldDataset.first().features.size
new RandomForestRegressionModel(trees, numFeatures)
}
@Since("1.4.0")
override def copy(extra: ParamMap): RandomForestRegressor = defaultCopy(extra)
}
@Since("1.4.0")
@Experimental
object RandomForestRegressor {
/** Accessor for supported impurity settings: variance */
@Since("1.4.0")
final val supportedImpurities: Array[String] = TreeRegressorParams.supportedImpurities
/** Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2 */
@Since("1.4.0")
final val supportedFeatureSubsetStrategies: Array[String] =
RandomForestParams.supportedFeatureSubsetStrategies
}
/**
* :: Experimental ::
* [[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.
* @param numFeatures Number of features used by this model
*/
@Since("1.4.0")
@Experimental
final class RandomForestRegressionModel private[ml] (
override val uid: String,
private val _trees: Array[DecisionTreeRegressionModel],
override val numFeatures: Int)
extends PredictionModel[Vector, RandomForestRegressionModel]
with TreeEnsembleModel with Serializable {
require(numTrees > 0, "RandomForestRegressionModel requires at least 1 tree.")
/**
* Construct a random forest regression model, with all trees weighted equally.
* @param trees Component trees
*/
private[ml] def this(trees: Array[DecisionTreeRegressionModel], numFeatures: Int) =
this(Identifiable.randomUID("rfr"), trees, numFeatures)
@Since("1.4.0")
override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
// Note: We may add support for weights (based on tree performance) later on.
private lazy val _treeWeights: Array[Double] = Array.fill[Double](numTrees)(1.0)
@Since("1.4.0")
override def treeWeights: Array[Double] = _treeWeights
override protected def transformImpl(dataset: DataFrame): DataFrame = {
val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
val predictUDF = udf { (features: Any) =>
bcastModel.value.predict(features.asInstanceOf[Vector])
}
dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
}
override protected def predict(features: Vector): Double = {
// TODO: When we add a generic Bagging class, handle transform there. SPARK-7128
// Predict average of tree predictions.
// Ignore the weights since all are 1.0 for now.
_trees.map(_.rootNode.predictImpl(features).prediction).sum / numTrees
}
@Since("1.4.0")
override def copy(extra: ParamMap): RandomForestRegressionModel = {
copyValues(new RandomForestRegressionModel(uid, _trees, numFeatures), extra).setParent(parent)
}
@Since("1.4.0")
override def toString: String = {
s"RandomForestRegressionModel (uid=$uid) with $numTrees trees"
}
/**
* Estimate of the importance of each feature.
*
* This generalizes the idea of "Gini" importance to other losses,
* following the explanation of Gini importance from "Random Forests" documentation
* by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.
*
* This feature importance is calculated as follows:
* - Average over trees:
* - importance(feature j) = sum (over nodes which split on feature j) of the gain,
* where gain is scaled by the number of instances passing through node
* - Normalize importances for tree to sum to 1.
* - Normalize feature importance vector to sum to 1.
*/
@Since("1.5.0")
lazy val featureImportances: Vector = RandomForest.featureImportances(trees, numFeatures)
/** (private[ml]) Convert to a model in the old API */
private[ml] def toOld: OldRandomForestModel = {
new OldRandomForestModel(OldAlgo.Regression, _trees.map(_.toOld))
}
}
private[ml] object RandomForestRegressionModel {
/** (private[ml]) Convert a model from the old API */
def fromOld(
oldModel: OldRandomForestModel,
parent: RandomForestRegressor,
categoricalFeatures: Map[Int, Int],
numFeatures: Int = -1): RandomForestRegressionModel = {
require(oldModel.algo == OldAlgo.Regression, "Cannot convert RandomForestModel" +
s" with algo=${oldModel.algo} (old API) to RandomForestRegressionModel (new API).")
val newTrees = oldModel.trees.map { tree =>
// parent for each tree is null since there is no good way to set this.
DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures)
}
new RandomForestRegressionModel(parent.uid, newTrees, numFeatures)
}
}
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