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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._
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, Strategy => OldStrategy}
import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions._
/**
* :: Experimental ::
* [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] learning algorithm
* for regression.
* It supports both continuous and categorical features.
*/
@Since("1.4.0")
@Experimental
final class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel]
with DecisionTreeRegressorParams {
@Since("1.4.0")
def this() = this(Identifiable.randomUID("dtr"))
// Override parameter setters from parent trait for Java API compatibility.
@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)
override def setSeed(value: Long): this.type = super.setSeed(value)
/** @group setParam */
def setVarianceCol(value: String): this.type = set(varianceCol, value)
override protected def train(dataset: DataFrame): DecisionTreeRegressionModel = {
val categoricalFeatures: Map[Int, Int] =
MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
val strategy = getOldStrategy(categoricalFeatures)
val trees = RandomForest.run(oldDataset, strategy, numTrees = 1, featureSubsetStrategy = "all",
seed = $(seed), parentUID = Some(uid))
trees.head.asInstanceOf[DecisionTreeRegressionModel]
}
/** (private[ml]) Create a Strategy instance to use with the old API. */
private[ml] def getOldStrategy(categoricalFeatures: Map[Int, Int]): OldStrategy = {
super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity,
subsamplingRate = 1.0)
}
@Since("1.4.0")
override def copy(extra: ParamMap): DecisionTreeRegressor = defaultCopy(extra)
}
@Since("1.4.0")
@Experimental
object DecisionTreeRegressor {
/** Accessor for supported impurities: variance */
final val supportedImpurities: Array[String] = TreeRegressorParams.supportedImpurities
}
/**
* :: Experimental ::
* [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] model for regression.
* It supports both continuous and categorical features.
* @param rootNode Root of the decision tree
*/
@Since("1.4.0")
@Experimental
final class DecisionTreeRegressionModel private[ml] (
override val uid: String,
override val rootNode: Node,
override val numFeatures: Int)
extends PredictionModel[Vector, DecisionTreeRegressionModel]
with DecisionTreeModel with DecisionTreeRegressorParams with Serializable {
/** @group setParam */
def setVarianceCol(value: String): this.type = set(varianceCol, value)
require(rootNode != null,
"DecisionTreeClassificationModel given null rootNode, but it requires a non-null rootNode.")
/**
* Construct a decision tree regression model.
* @param rootNode Root node of tree, with other nodes attached.
*/
private[ml] def this(rootNode: Node, numFeatures: Int) =
this(Identifiable.randomUID("dtr"), rootNode, numFeatures)
override protected def predict(features: Vector): Double = {
rootNode.predictImpl(features).prediction
}
/** We need to update this function if we ever add other impurity measures. */
protected def predictVariance(features: Vector): Double = {
rootNode.predictImpl(features).impurityStats.calculate()
}
override def transform(dataset: DataFrame): DataFrame = {
transformSchema(dataset.schema, logging = true)
transformImpl(dataset)
}
override protected def transformImpl(dataset: DataFrame): DataFrame = {
val predictUDF = udf { (features: Vector) => predict(features) }
val predictVarianceUDF = udf { (features: Vector) => predictVariance(features) }
var output = dataset
if ($(predictionCol).nonEmpty) {
output = output.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
}
if (isDefined(varianceCol) && $(varianceCol).nonEmpty) {
output = output.withColumn($(varianceCol), predictVarianceUDF(col($(featuresCol))))
}
output
}
@Since("1.4.0")
override def copy(extra: ParamMap): DecisionTreeRegressionModel = {
copyValues(new DecisionTreeRegressionModel(uid, rootNode, numFeatures), extra).setParent(parent)
}
@Since("1.4.0")
override def toString: String = {
s"DecisionTreeRegressionModel (uid=$uid) of depth $depth with $numNodes nodes"
}
/** Convert to a model in the old API */
private[ml] def toOld: OldDecisionTreeModel = {
new OldDecisionTreeModel(rootNode.toOld(1), OldAlgo.Regression)
}
}
private[ml] object DecisionTreeRegressionModel {
/** (private[ml]) Convert a model from the old API */
def fromOld(
oldModel: OldDecisionTreeModel,
parent: DecisionTreeRegressor,
categoricalFeatures: Map[Int, Int],
numFeatures: Int = -1): DecisionTreeRegressionModel = {
require(oldModel.algo == OldAlgo.Regression,
s"Cannot convert non-regression DecisionTreeModel (old API) to" +
s" DecisionTreeRegressionModel (new API). Algo is: ${oldModel.algo}")
val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures)
val uid = if (parent != null) parent.uid else Identifiable.randomUID("dtr")
new DecisionTreeRegressionModel(uid, rootNode, numFeatures)
}
}
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