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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.json4s.{DefaultFormats, JObject}
import org.json4s.JsonDSL._
import org.apache.spark.annotation.{Experimental, Since}
import org.apache.spark.internal.Logging
import org.apache.spark.ml.{PredictionModel, Predictor}
import org.apache.spark.ml.feature.LabeledPoint
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.impl.GradientBoostedTrees
import org.apache.spark.ml.util._
import org.apache.spark.ml.util.DefaultParamsReader.Metadata
import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Dataset}
import org.apache.spark.sql.functions._
/**
* :: Experimental ::
* [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]]
* learning algorithm for regression.
* It supports both continuous and categorical features.
*
* The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.
*
* Notes on Gradient Boosting vs. TreeBoost:
* - This implementation is for Stochastic Gradient Boosting, not for TreeBoost.
* - Both algorithms learn tree ensembles by minimizing loss functions.
* - TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes
* based on the loss function, whereas the original gradient boosting method does not.
* - When the loss is SquaredError, these methods give the same result, but they could differ
* for other loss functions.
* - We expect to implement TreeBoost in the future:
* [https://issues.apache.org/jira/browse/SPARK-4240]
*/
@Since("1.4.0")
@Experimental
class GBTRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
extends Predictor[Vector, GBTRegressor, GBTRegressionModel]
with GBTRegressorParams with DefaultParamsWritable with Logging {
@Since("1.4.0")
def this() = this(Identifiable.randomUID("gbtr"))
// 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)
/**
* The impurity setting is ignored for GBT models.
* Individual trees are built using impurity "Variance."
*/
@Since("1.4.0")
override def setImpurity(value: String): this.type = {
logWarning("GBTRegressor.setImpurity should NOT be used")
this
}
// 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 GBTParams:
@Since("1.4.0")
override def setMaxIter(value: Int): this.type = super.setMaxIter(value)
@Since("1.4.0")
override def setStepSize(value: Double): this.type = super.setStepSize(value)
// Parameters from GBTRegressorParams:
/** @group setParam */
@Since("1.4.0")
def setLossType(value: String): this.type = set(lossType, value)
override protected def train(dataset: Dataset[_]): GBTRegressionModel = {
val categoricalFeatures: Map[Int, Int] =
MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
val numFeatures = oldDataset.first().features.size
val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Regression)
val (baseLearners, learnerWeights) = GradientBoostedTrees.run(oldDataset, boostingStrategy,
$(seed))
new GBTRegressionModel(uid, baseLearners, learnerWeights, numFeatures)
}
@Since("1.4.0")
override def copy(extra: ParamMap): GBTRegressor = defaultCopy(extra)
}
@Since("1.4.0")
@Experimental
object GBTRegressor extends DefaultParamsReadable[GBTRegressor] {
/** Accessor for supported loss settings: squared (L2), absolute (L1) */
@Since("1.4.0")
final val supportedLossTypes: Array[String] = GBTRegressorParams.supportedLossTypes
@Since("2.0.0")
override def load(path: String): GBTRegressor = super.load(path)
}
/**
* :: Experimental ::
*
* [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]]
* model for regression.
* It supports both continuous and categorical features.
* @param _trees Decision trees in the ensemble.
* @param _treeWeights Weights for the decision trees in the ensemble.
*/
@Since("1.4.0")
@Experimental
class GBTRegressionModel private[ml](
override val uid: String,
private val _trees: Array[DecisionTreeRegressionModel],
private val _treeWeights: Array[Double],
override val numFeatures: Int)
extends PredictionModel[Vector, GBTRegressionModel]
with GBTRegressorParams with TreeEnsembleModel[DecisionTreeRegressionModel]
with MLWritable with Serializable {
require(_trees.nonEmpty, "GBTRegressionModel requires at least 1 tree.")
require(_trees.length == _treeWeights.length, "GBTRegressionModel given trees, treeWeights of" +
s" non-matching lengths (${_trees.length}, ${_treeWeights.length}, respectively).")
/**
* Construct a GBTRegressionModel
* @param _trees Decision trees in the ensemble.
* @param _treeWeights Weights for the decision trees in the ensemble.
*/
@Since("1.4.0")
def this(uid: String, _trees: Array[DecisionTreeRegressionModel], _treeWeights: Array[Double]) =
this(uid, _trees, _treeWeights, -1)
@Since("1.4.0")
override def trees: Array[DecisionTreeRegressionModel] = _trees
@Since("1.4.0")
override def treeWeights: Array[Double] = _treeWeights
override protected def transformImpl(dataset: Dataset[_]): DataFrame = {
val bcastModel = dataset.sparkSession.sparkContext.broadcast(this)
val predictUDF = udf { (features: Any) =>
bcastModel.value.predict(features.asInstanceOf[Vector])
}
dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
}
override protected def predict(features: Vector): Double = {
// TODO: When we add a generic Boosting class, handle transform there? SPARK-7129
// Classifies by thresholding sum of weighted tree predictions
val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
}
/** Number of trees in ensemble */
val numTrees: Int = trees.length
@Since("1.4.0")
override def copy(extra: ParamMap): GBTRegressionModel = {
copyValues(new GBTRegressionModel(uid, _trees, _treeWeights, numFeatures),
extra).setParent(parent)
}
@Since("1.4.0")
override def toString: String = {
s"GBTRegressionModel (uid=$uid) with $numTrees trees"
}
/**
* Estimate of the importance of each feature.
*
* Each feature's importance is the average of its importance across all trees in the ensemble
* The importance vector is normalized to sum to 1. This method is suggested by Hastie et al.
* (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.)
* and follows the implementation from scikit-learn.
*
* @see [[DecisionTreeRegressionModel.featureImportances]]
*/
@Since("2.0.0")
lazy val featureImportances: Vector = TreeEnsembleModel.featureImportances(trees, numFeatures)
/** (private[ml]) Convert to a model in the old API */
private[ml] def toOld: OldGBTModel = {
new OldGBTModel(OldAlgo.Regression, _trees.map(_.toOld), _treeWeights)
}
@Since("2.0.0")
override def write: MLWriter = new GBTRegressionModel.GBTRegressionModelWriter(this)
}
@Since("2.0.0")
object GBTRegressionModel extends MLReadable[GBTRegressionModel] {
@Since("2.0.0")
override def read: MLReader[GBTRegressionModel] = new GBTRegressionModelReader
@Since("2.0.0")
override def load(path: String): GBTRegressionModel = super.load(path)
private[GBTRegressionModel]
class GBTRegressionModelWriter(instance: GBTRegressionModel) extends MLWriter {
override protected def saveImpl(path: String): Unit = {
val extraMetadata: JObject = Map(
"numFeatures" -> instance.numFeatures,
"numTrees" -> instance.getNumTrees)
EnsembleModelReadWrite.saveImpl(instance, path, sqlContext, extraMetadata)
}
}
private class GBTRegressionModelReader extends MLReader[GBTRegressionModel] {
/** Checked against metadata when loading model */
private val className = classOf[GBTRegressionModel].getName
private val treeClassName = classOf[DecisionTreeRegressionModel].getName
override def load(path: String): GBTRegressionModel = {
implicit val format = DefaultFormats
val (metadata: Metadata, treesData: Array[(Metadata, Node)], treeWeights: Array[Double]) =
EnsembleModelReadWrite.loadImpl(path, sqlContext, className, treeClassName)
val numFeatures = (metadata.metadata \ "numFeatures").extract[Int]
val numTrees = (metadata.metadata \ "numTrees").extract[Int]
val trees: Array[DecisionTreeRegressionModel] = treesData.map {
case (treeMetadata, root) =>
val tree =
new DecisionTreeRegressionModel(treeMetadata.uid, root, numFeatures)
DefaultParamsReader.getAndSetParams(tree, treeMetadata)
tree
}
require(numTrees == trees.length, s"GBTRegressionModel.load expected $numTrees" +
s" trees based on metadata but found ${trees.length} trees.")
val model = new GBTRegressionModel(metadata.uid, trees, treeWeights, numFeatures)
DefaultParamsReader.getAndSetParams(model, metadata)
model
}
}
/** Convert a model from the old API */
private[ml] def fromOld(
oldModel: OldGBTModel,
parent: GBTRegressor,
categoricalFeatures: Map[Int, Int],
numFeatures: Int = -1): GBTRegressionModel = {
require(oldModel.algo == OldAlgo.Regression, "Cannot convert GradientBoostedTreesModel" +
s" with algo=${oldModel.algo} (old API) to GBTRegressionModel (new API).")
val newTrees = oldModel.trees.map { tree =>
// parent for each tree is null since there is no good way to set this.
DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures)
}
val uid = if (parent != null) parent.uid else Identifiable.randomUID("gbtr")
new GBTRegressionModel(uid, newTrees, oldModel.treeWeights, numFeatures)
}
}
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