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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 com.github.fommil.netlib.BLAS.{getInstance => blas}
import org.apache.spark.Logging
import org.apache.spark.annotation.AlphaComponent
import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
import org.apache.spark.ml.impl.tree._
import org.apache.spark.ml.param.{Param, 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.{GradientBoostedTrees => OldGBT}
import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, Loss => OldLoss, SquaredError => OldSquaredError}
import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.DataFrame
/**
* :: AlphaComponent ::
*
* [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]]
* learning algorithm for regression.
* It supports both continuous and categorical features.
*/
@AlphaComponent
final class GBTRegressor
extends Predictor[Vector, GBTRegressor, GBTRegressionModel]
with GBTParams with TreeRegressorParams with Logging {
// 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)
/**
* The impurity setting is ignored for GBT models.
* Individual trees are built using impurity "Variance."
*/
override def setImpurity(value: String): this.type = {
logWarning("GBTRegressor.setImpurity should NOT be used")
this
}
// Parameters from TreeEnsembleParams:
override def setSubsamplingRate(value: Double): this.type = super.setSubsamplingRate(value)
override def setSeed(value: Long): this.type = {
logWarning("The 'seed' parameter is currently ignored by Gradient Boosting.")
super.setSeed(value)
}
// Parameters from GBTParams:
override def setMaxIter(value: Int): this.type = super.setMaxIter(value)
override def setStepSize(value: Double): this.type = super.setStepSize(value)
// Parameters for GBTRegressor:
/**
* Loss function which GBT tries to minimize. (case-insensitive)
* Supported: "squared" (L2) and "absolute" (L1)
* (default = squared)
* @group param
*/
val lossType: Param[String] = new Param[String](this, "lossType", "Loss function which GBT" +
" tries to minimize (case-insensitive). Supported options:" +
s" ${GBTRegressor.supportedLossTypes.mkString(", ")}",
(value: String) => GBTRegressor.supportedLossTypes.contains(value.toLowerCase))
setDefault(lossType -> "squared")
/** @group setParam */
def setLossType(value: String): this.type = set(lossType, value)
/** @group getParam */
def getLossType: String = $(lossType).toLowerCase
/** (private[ml]) Convert new loss to old loss. */
override private[ml] def getOldLossType: OldLoss = {
getLossType match {
case "squared" => OldSquaredError
case "absolute" => OldAbsoluteError
case _ =>
// Should never happen because of check in setter method.
throw new RuntimeException(s"GBTRegressorParams was given bad loss type: $getLossType")
}
}
override protected def train(dataset: DataFrame): GBTRegressionModel = {
val categoricalFeatures: Map[Int, Int] =
MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Regression)
val oldGBT = new OldGBT(boostingStrategy)
val oldModel = oldGBT.run(oldDataset)
GBTRegressionModel.fromOld(oldModel, this, categoricalFeatures)
}
}
object GBTRegressor {
// The losses below should be lowercase.
/** Accessor for supported loss settings: squared (L2), absolute (L1) */
final val supportedLossTypes: Array[String] = Array("squared", "absolute").map(_.toLowerCase)
}
/**
* :: AlphaComponent ::
*
* [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]]
* model for regression.
* It supports both continuous and categorical features.
* @param _trees Decision trees in the ensemble.
* @param _treeWeights Weights for the decision trees in the ensemble.
*/
@AlphaComponent
final class GBTRegressionModel(
override val parent: GBTRegressor,
private val _trees: Array[DecisionTreeRegressionModel],
private val _treeWeights: Array[Double])
extends PredictionModel[Vector, GBTRegressionModel]
with TreeEnsembleModel with Serializable {
require(numTrees > 0, "GBTRegressionModel requires at least 1 tree.")
require(_trees.length == _treeWeights.length, "GBTRegressionModel given trees, treeWeights of" +
s" non-matching lengths (${_trees.length}, ${_treeWeights.length}, respectively).")
override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
override def treeWeights: Array[Double] = _treeWeights
override protected def predict(features: Vector): Double = {
// TODO: Override transform() to broadcast model. SPARK-7127
// TODO: When we add a generic Boosting class, handle transform there? SPARK-7129
// Classifies by thresholding sum of weighted tree predictions
val treePredictions = _trees.map(_.rootNode.predict(features))
val prediction = blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
if (prediction > 0.0) 1.0 else 0.0
}
override def copy(extra: ParamMap): GBTRegressionModel = {
copyValues(new GBTRegressionModel(parent, _trees, _treeWeights), extra)
}
override def toString: String = {
s"GBTRegressionModel with $numTrees trees"
}
/** (private[ml]) Convert to a model in the old API */
private[ml] def toOld: OldGBTModel = {
new OldGBTModel(OldAlgo.Regression, _trees.map(_.toOld), _treeWeights)
}
}
private[ml] object GBTRegressionModel {
/** (private[ml]) Convert a model from the old API */
def fromOld(
oldModel: OldGBTModel,
parent: GBTRegressor,
categoricalFeatures: Map[Int, Int]): GBTRegressionModel = {
require(oldModel.algo == OldAlgo.Regression, "Cannot convert GradientBoostedTreesModel" +
s" with algo=${oldModel.algo} (old API) to GBTRegressionModel (new API).")
val newTrees = oldModel.trees.map { tree =>
// parent, fittingParamMap for each tree is null since there are no good ways to set these.
DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures)
}
new GBTRegressionModel(parent, newTrees, oldModel.treeWeights)
}
}
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