spark DecisionTreeRegressor 源码

spark DecisionTreeRegressor 代码

文件路径：/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala

/*
 * 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.hadoop.fs.Path
import org.json4s.{DefaultFormats, JObject}
import org.json4s.JsonDSL._

import org.apache.spark.annotation.Since
import org.apache.spark.ml.feature.Instance
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.tree._
import org.apache.spark.ml.tree.DecisionTreeModelReadWrite._
import org.apache.spark.ml.tree.impl.RandomForest
import org.apache.spark.ml.util._
import org.apache.spark.ml.util.DatasetUtils._
import org.apache.spark.ml.util.Instrumentation.instrumented
import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel}
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.StructType

/**
 * <a href="http://en.wikipedia.org/wiki/Decision_tree_learning">Decision tree</a>
 * learning algorithm for regression.
 * It supports both continuous and categorical features.
 */
@Since("1.4.0")
class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
  extends Regressor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel]
  with DecisionTreeRegressorParams with DefaultParamsWritable {

  @Since("1.4.0")
  def this() = this(Identifiable.randomUID("dtr"))

  // Override parameter setters from parent trait for Java API compatibility.
  /** @group setParam */
  @Since("1.4.0")
  def setMaxDepth(value: Int): this.type = set(maxDepth, value)

  /** @group setParam */
  @Since("1.4.0")
  def setMaxBins(value: Int): this.type = set(maxBins, value)

  /** @group setParam */
  @Since("1.4.0")
  def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)

  /** @group setParam */
  @Since("3.0.0")
  def setMinWeightFractionPerNode(value: Double): this.type = set(minWeightFractionPerNode, value)

  @Since("1.4.0")
  def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)

  /** @group expertSetParam */
  @Since("1.4.0")
  def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value)

  /** @group expertSetParam */
  @Since("1.4.0")
  def setCacheNodeIds(value: Boolean): this.type = set(cacheNodeIds, value)

  /**
   * Specifies how often to checkpoint the cached node IDs.
   * E.g. 10 means that the cache will get checkpointed every 10 iterations.
   * This is only used if cacheNodeIds is true and if the checkpoint directory is set in
   * [[org.apache.spark.SparkContext]].
   * Must be at least 1.
   * (default = 10)
   * @group setParam
   */
  @Since("1.4.0")
  def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)

  /** @group setParam */
  @Since("1.4.0")
  def setImpurity(value: String): this.type = set(impurity, value)

  /** @group setParam */
  @Since("1.6.0")
  def setSeed(value: Long): this.type = set(seed, value)

  /** @group setParam */
  @Since("2.0.0")
  def setVarianceCol(value: String): this.type = set(varianceCol, value)

  /**
   * Sets the value of param [[weightCol]].
   * If this is not set or empty, we treat all instance weights as 1.0.
   * Default is not set, so all instances have weight one.
   *
   * @group setParam
   */
  @Since("3.0.0")
  def setWeightCol(value: String): this.type = set(weightCol, value)

  override protected def train(
      dataset: Dataset[_]): DecisionTreeRegressionModel = instrumented { instr =>
    val categoricalFeatures: Map[Int, Int] =
      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))

    val instances = dataset.select(
      checkRegressionLabels($(labelCol)),
      checkNonNegativeWeights(get(weightCol)),
      checkNonNanVectors($(featuresCol))
    ).rdd.map { case Row(l: Double, w: Double, v: Vector) => Instance(l, w, v)
    }.setName("training instances")

    val strategy = getOldStrategy(categoricalFeatures)
    require(!strategy.bootstrap, "DecisionTreeRegressor does not need bootstrap sampling")

    instr.logPipelineStage(this)
    instr.logDataset(instances)
    instr.logParams(this, params: _*)

    val trees = RandomForest.run(instances, strategy, numTrees = 1, featureSubsetStrategy = "all",
      seed = $(seed), instr = Some(instr), 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")
object DecisionTreeRegressor extends DefaultParamsReadable[DecisionTreeRegressor] {
  /** Accessor for supported impurities: variance */
  final val supportedImpurities: Array[String] = HasVarianceImpurity.supportedImpurities

  @Since("2.0.0")
  override def load(path: String): DecisionTreeRegressor = super.load(path)
}

/**
 * <a href="http://en.wikipedia.org/wiki/Decision_tree_learning">
 * Decision tree (Wikipedia)</a> model for regression.
 * It supports both continuous and categorical features.
 *
 * @param rootNode  Root of the decision tree
 */
@Since("1.4.0")
class DecisionTreeRegressionModel private[ml] (
    override val uid: String,
    override val rootNode: Node,
    override val numFeatures: Int)
  extends RegressionModel[Vector, DecisionTreeRegressionModel]
  with DecisionTreeModel with DecisionTreeRegressorParams with MLWritable with Serializable {

  /** @group setParam */
  def setVarianceCol(value: String): this.type = set(varianceCol, value)

  require(rootNode != null,
    "DecisionTreeRegressionModel 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 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()
  }

  @Since("1.4.0")
  override def transformSchema(schema: StructType): StructType = {
    var outputSchema = super.transformSchema(schema)
    if (isDefined(varianceCol) && $(varianceCol).nonEmpty) {
      outputSchema = SchemaUtils.updateNumeric(outputSchema, $(varianceCol))
    }
    if ($(leafCol).nonEmpty) {
      outputSchema = SchemaUtils.updateField(outputSchema, getLeafField($(leafCol)))
    }
    outputSchema
  }

  @Since("2.0.0")
  override def transform(dataset: Dataset[_]): DataFrame = {
    val outputSchema = transformSchema(dataset.schema, logging = true)

    var predictionColNames = Seq.empty[String]
    var predictionColumns = Seq.empty[Column]

    if ($(predictionCol).nonEmpty) {
      val predictUDF = udf { features: Vector => predict(features) }
      predictionColNames :+= $(predictionCol)
      predictionColumns :+= predictUDF(col($(featuresCol)))
        .as($(predictionCol), outputSchema($(predictionCol)).metadata)
    }

    if (isDefined(varianceCol) && $(varianceCol).nonEmpty) {
      val predictVarianceUDF = udf { features: Vector => predictVariance(features) }
      predictionColNames :+= $(varianceCol)
      predictionColumns :+= predictVarianceUDF(col($(featuresCol)))
        .as($(varianceCol), outputSchema($(varianceCol)).metadata)
    }

    if ($(leafCol).nonEmpty) {
      val leafUDF = udf { features: Vector => predictLeaf(features) }
      predictionColNames :+= $(leafCol)
      predictionColumns :+= leafUDF(col($(featuresCol)))
        .as($(leafCol), outputSchema($(leafCol)).metadata)
    }

    if (predictionColNames.nonEmpty) {
      dataset.withColumns(predictionColNames, predictionColumns)
    } else {
      this.logWarning(s"$uid: DecisionTreeRegressionModel.transform() does nothing" +
        " because no output columns were set.")
      dataset.toDF()
    }
  }

  @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=$uid, depth=$depth, numNodes=$numNodes, " +
      s"numFeatures=$numFeatures"
  }

  /**
   * 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:
   *   - 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.
   *
   * @note Feature importance for single decision trees can have high variance due to
   * correlated predictor variables. Consider using a [[RandomForestRegressor]]
   * to determine feature importance instead.
   */
  @Since("2.0.0")
  lazy val featureImportances: Vector = TreeEnsembleModel.featureImportances(this, numFeatures)

  /** Convert to spark.mllib DecisionTreeModel (losing some information) */
  override private[spark] def toOld: OldDecisionTreeModel = {
    new OldDecisionTreeModel(rootNode.toOld(1), OldAlgo.Regression)
  }

  @Since("2.0.0")
  override def write: MLWriter =
    new DecisionTreeRegressionModel.DecisionTreeRegressionModelWriter(this)
}

@Since("2.0.0")
object DecisionTreeRegressionModel extends MLReadable[DecisionTreeRegressionModel] {

  @Since("2.0.0")
  override def read: MLReader[DecisionTreeRegressionModel] =
    new DecisionTreeRegressionModelReader

  @Since("2.0.0")
  override def load(path: String): DecisionTreeRegressionModel = super.load(path)

  private[DecisionTreeRegressionModel]
  class DecisionTreeRegressionModelWriter(instance: DecisionTreeRegressionModel)
    extends MLWriter {

    override protected def saveImpl(path: String): Unit = {
      val extraMetadata: JObject = Map(
        "numFeatures" -> instance.numFeatures)
      DefaultParamsWriter.saveMetadata(instance, path, sc, Some(extraMetadata))
      val (nodeData, _) = NodeData.build(instance.rootNode, 0)
      val dataPath = new Path(path, "data").toString
      val numDataParts = NodeData.inferNumPartitions(instance.numNodes)
      sparkSession.createDataFrame(nodeData).repartition(numDataParts).write.parquet(dataPath)
    }
  }

  private class DecisionTreeRegressionModelReader
    extends MLReader[DecisionTreeRegressionModel] {

    /** Checked against metadata when loading model */
    private val className = classOf[DecisionTreeRegressionModel].getName

    override def load(path: String): DecisionTreeRegressionModel = {
      implicit val format = DefaultFormats
      val metadata = DefaultParamsReader.loadMetadata(path, sc, className)
      val numFeatures = (metadata.metadata \ "numFeatures").extract[Int]
      val root = loadTreeNodes(path, metadata, sparkSession)
      val model = new DecisionTreeRegressionModel(metadata.uid, root, numFeatures)
      metadata.getAndSetParams(model)
      model
    }
  }

  /** Convert a model from the old API */
  private[ml] 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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