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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.classification
import org.apache.spark.annotation.Experimental
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeClassifierParams}
import org.apache.spark.ml.tree.impl.RandomForest
import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
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
/**
* :: Experimental ::
* [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] learning algorithm
* for classification.
* It supports both binary and multiclass labels, as well as both continuous and categorical
* features.
*/
@Experimental
final class DecisionTreeClassifier(override val uid: String)
extends ProbabilisticClassifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel]
with DecisionTreeParams with TreeClassifierParams {
def this() = this(Identifiable.randomUID("dtc"))
// Override parameter setters from parent trait for Java API compatibility.
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)
override protected def train(dataset: DataFrame): DecisionTreeClassificationModel = {
val categoricalFeatures: Map[Int, Int] =
MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match {
case Some(n: Int) => n
case None => throw new IllegalArgumentException("DecisionTreeClassifier was given input" +
s" with invalid label column ${$(labelCol)}, without the number of classes" +
" specified. See StringIndexer.")
// TODO: Automatically index labels: SPARK-7126
}
val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
val strategy = getOldStrategy(categoricalFeatures, numClasses)
val trees = RandomForest.run(oldDataset, strategy, numTrees = 1, featureSubsetStrategy = "all",
seed = 0L, parentUID = Some(uid))
trees.head.asInstanceOf[DecisionTreeClassificationModel]
}
/** (private[ml]) Create a Strategy instance to use with the old API. */
private[ml] def getOldStrategy(
categoricalFeatures: Map[Int, Int],
numClasses: Int): OldStrategy = {
super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity,
subsamplingRate = 1.0)
}
override def copy(extra: ParamMap): DecisionTreeClassifier = defaultCopy(extra)
}
@Experimental
object DecisionTreeClassifier {
/** Accessor for supported impurities: entropy, gini */
final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities
}
/**
* :: Experimental ::
* [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] model for classification.
* It supports both binary and multiclass labels, as well as both continuous and categorical
* features.
*/
@Experimental
final class DecisionTreeClassificationModel private[ml] (
override val uid: String,
override val rootNode: Node,
override val numClasses: Int)
extends ProbabilisticClassificationModel[Vector, DecisionTreeClassificationModel]
with DecisionTreeModel with Serializable {
require(rootNode != null,
"DecisionTreeClassificationModel given null rootNode, but it requires a non-null rootNode.")
/**
* Construct a decision tree classification model.
* @param rootNode Root node of tree, with other nodes attached.
*/
private[ml] def this(rootNode: Node, numClasses: Int) =
this(Identifiable.randomUID("dtc"), rootNode, numClasses)
override protected def predict(features: Vector): Double = {
rootNode.predictImpl(features).prediction
}
override protected def predictRaw(features: Vector): Vector = {
Vectors.dense(rootNode.predictImpl(features).impurityStats.stats.clone())
}
override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
rawPrediction match {
case dv: DenseVector =>
ProbabilisticClassificationModel.normalizeToProbabilitiesInPlace(dv)
dv
case sv: SparseVector =>
throw new RuntimeException("Unexpected error in DecisionTreeClassificationModel:" +
" raw2probabilityInPlace encountered SparseVector")
}
}
override def copy(extra: ParamMap): DecisionTreeClassificationModel = {
copyValues(new DecisionTreeClassificationModel(uid, rootNode, numClasses), extra)
}
override def toString: String = {
s"DecisionTreeClassificationModel of depth $depth with $numNodes nodes"
}
/** (private[ml]) Convert to a model in the old API */
private[ml] def toOld: OldDecisionTreeModel = {
new OldDecisionTreeModel(rootNode.toOld(1), OldAlgo.Classification)
}
}
private[ml] object DecisionTreeClassificationModel {
/** (private[ml]) Convert a model from the old API */
def fromOld(
oldModel: OldDecisionTreeModel,
parent: DecisionTreeClassifier,
categoricalFeatures: Map[Int, Int]): DecisionTreeClassificationModel = {
require(oldModel.algo == OldAlgo.Classification,
s"Cannot convert non-classification DecisionTreeModel (old API) to" +
s" DecisionTreeClassificationModel (new API). Algo is: ${oldModel.algo}")
val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures)
val uid = if (parent != null) parent.uid else Identifiable.randomUID("dtc")
new DecisionTreeClassificationModel(uid, rootNode, -1)
}
}
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