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examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostedTrees.java
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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. | ||
| */ | ||
|
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| package org.apache.spark.examples.mllib; | ||
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| import scala.Tuple2; | ||
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| import org.apache.spark.SparkConf; | ||
| import org.apache.spark.api.java.JavaPairRDD; | ||
| import org.apache.spark.api.java.JavaRDD; | ||
| import org.apache.spark.api.java.JavaSparkContext; | ||
| import org.apache.spark.api.java.function.Function; | ||
| import org.apache.spark.api.java.function.Function2; | ||
| import org.apache.spark.api.java.function.PairFunction; | ||
| import org.apache.spark.mllib.regression.LabeledPoint; | ||
| import org.apache.spark.mllib.tree.GradientBoosting; | ||
| import org.apache.spark.mllib.tree.configuration.BoostingStrategy; | ||
| import org.apache.spark.mllib.tree.model.WeightedEnsembleModel; | ||
| import org.apache.spark.mllib.util.MLUtils; | ||
|
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| /** | ||
| * Classification and regression using gradient-boosted decision trees. | ||
| */ | ||
| public final class JavaGradientBoostedTrees { | ||
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| private static void usage() { | ||
| System.err.println("Usage: JavaGradientBoostedTrees <libsvm format data file>" + | ||
| " <Classification/Regression>"); | ||
| System.exit(-1); | ||
| } | ||
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| public static void main(String[] args) { | ||
| String datapath = "data/mllib/sample_libsvm_data.txt"; | ||
| String algo = "Classification"; | ||
| if (args.length >= 1) { | ||
| datapath = args[0]; | ||
| } | ||
| if (args.length >= 2) { | ||
| algo = args[1]; | ||
| } | ||
| if (args.length > 2) { | ||
| usage(); | ||
| } | ||
| SparkConf sparkConf = new SparkConf().setAppName("JavaGradientBoostedTrees"); | ||
| JavaSparkContext sc = new JavaSparkContext(sparkConf); | ||
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| JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD().cache(); | ||
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| // Set parameters. | ||
| // Note: All features are treated as continuous. | ||
| BoostingStrategy boostingStrategy = BoostingStrategy.defaultParams(algo); | ||
| boostingStrategy.setNumIterations(10); | ||
| boostingStrategy.weakLearnerParams().setMaxDepth(5); | ||
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| if (algo.equals("Classification")) { | ||
| // Compute the number of classes from the data. | ||
| Integer numClasses = data.map(new Function<LabeledPoint, Double>() { | ||
| @Override public Double call(LabeledPoint p) { | ||
| return p.label(); | ||
| } | ||
| }).countByValue().size(); | ||
| boostingStrategy.setNumClassesForClassification(numClasses); // ignored for Regression | ||
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| // Train a GradientBoosting model for classification. | ||
| final WeightedEnsembleModel model = GradientBoosting.trainClassifier(data, boostingStrategy); | ||
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| // Evaluate model on training instances and compute training error | ||
| JavaPairRDD<Double, Double> predictionAndLabel = | ||
| data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() { | ||
| @Override public Tuple2<Double, Double> call(LabeledPoint p) { | ||
| return new Tuple2<Double, Double>(model.predict(p.features()), p.label()); | ||
| } | ||
| }); | ||
| Double trainErr = | ||
| 1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() { | ||
| @Override public Boolean call(Tuple2<Double, Double> pl) { | ||
| return !pl._1().equals(pl._2()); | ||
| } | ||
| }).count() / data.count(); | ||
| System.out.println("Training error: " + trainErr); | ||
| System.out.println("Learned classification tree model:\n" + model); | ||
| } else if (algo.equals("Regression")) { | ||
| // Train a GradientBoosting model for classification. | ||
| final WeightedEnsembleModel model = GradientBoosting.trainRegressor(data, boostingStrategy); | ||
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| // Evaluate model on training instances and compute training error | ||
| JavaPairRDD<Double, Double> predictionAndLabel = | ||
| data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() { | ||
| @Override public Tuple2<Double, Double> call(LabeledPoint p) { | ||
| return new Tuple2<Double, Double>(model.predict(p.features()), p.label()); | ||
| } | ||
| }); | ||
| Double trainMSE = | ||
| predictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() { | ||
| @Override public Double call(Tuple2<Double, Double> pl) { | ||
| Double diff = pl._1() - pl._2(); | ||
| return diff * diff; | ||
| } | ||
| }).reduce(new Function2<Double, Double, Double>() { | ||
| @Override public Double call(Double a, Double b) { | ||
| return a + b; | ||
| } | ||
| }) / data.count(); | ||
| System.out.println("Training Mean Squared Error: " + trainMSE); | ||
| System.out.println("Learned regression tree model:\n" + model); | ||
| } else { | ||
| usage(); | ||
| } | ||
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| sc.stop(); | ||
| } | ||
| } |
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examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTrees.scala
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,146 @@ | ||
| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.examples.mllib | ||
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| import scopt.OptionParser | ||
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| import org.apache.spark.{SparkConf, SparkContext} | ||
| import org.apache.spark.mllib.evaluation.MulticlassMetrics | ||
| import org.apache.spark.mllib.tree.GradientBoosting | ||
| import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Algo} | ||
| import org.apache.spark.util.Utils | ||
|
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| /** | ||
| * An example runner for Gradient Boosting using decision trees as weak learners. Run with | ||
| * {{{ | ||
| * ./bin/run-example org.apache.spark.examples.mllib.GradientBoostedTrees [options] | ||
| * }}} | ||
| * If you use it as a template to create your own app, please use `spark-submit` to submit your app. | ||
| * | ||
| * Note: This script treats all features as real-valued (not categorical). | ||
| * To include categorical features, modify categoricalFeaturesInfo. | ||
| */ | ||
| object GradientBoostedTrees { | ||
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| case class Params( | ||
| input: String = null, | ||
| testInput: String = "", | ||
| dataFormat: String = "libsvm", | ||
| algo: String = "Classification", | ||
| maxDepth: Int = 5, | ||
| numIterations: Int = 10, | ||
| fracTest: Double = 0.2) extends AbstractParams[Params] | ||
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| def main(args: Array[String]) { | ||
| val defaultParams = Params() | ||
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| val parser = new OptionParser[Params]("GradientBoostedTrees") { | ||
| head("GradientBoostedTrees: an example decision tree app.") | ||
| opt[String]("algo") | ||
| .text(s"algorithm (${Algo.values.mkString(",")}), default: ${defaultParams.algo}") | ||
| .action((x, c) => c.copy(algo = x)) | ||
| opt[Int]("maxDepth") | ||
| .text(s"max depth of the tree, default: ${defaultParams.maxDepth}") | ||
| .action((x, c) => c.copy(maxDepth = x)) | ||
| opt[Int]("numIterations") | ||
| .text(s"number of iterations of boosting," + s" default: ${defaultParams.numIterations}") | ||
| .action((x, c) => c.copy(numIterations = x)) | ||
| opt[Double]("fracTest") | ||
| .text(s"fraction of data to hold out for testing. If given option testInput, " + | ||
| s"this option is ignored. default: ${defaultParams.fracTest}") | ||
| .action((x, c) => c.copy(fracTest = x)) | ||
| opt[String]("testInput") | ||
| .text(s"input path to test dataset. If given, option fracTest is ignored." + | ||
| s" default: ${defaultParams.testInput}") | ||
| .action((x, c) => c.copy(testInput = x)) | ||
| opt[String]("<dataFormat>") | ||
| .text("data format: libsvm (default), dense (deprecated in Spark v1.1)") | ||
| .action((x, c) => c.copy(dataFormat = x)) | ||
| arg[String]("<input>") | ||
| .text("input path to labeled examples") | ||
| .required() | ||
| .action((x, c) => c.copy(input = x)) | ||
| checkConfig { params => | ||
| if (params.fracTest < 0 || params.fracTest > 1) { | ||
| failure(s"fracTest ${params.fracTest} value incorrect; should be in [0,1].") | ||
| } else { | ||
| success | ||
| } | ||
| } | ||
| } | ||
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| parser.parse(args, defaultParams).map { params => | ||
| run(params) | ||
| }.getOrElse { | ||
| sys.exit(1) | ||
| } | ||
| } | ||
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| def run(params: Params) { | ||
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| val conf = new SparkConf().setAppName(s"GradientBoostedTrees with $params") | ||
| val sc = new SparkContext(conf) | ||
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| println(s"GradientBoostedTrees with parameters:\n$params") | ||
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| // Load training and test data and cache it. | ||
| val (training, test, numClasses) = DecisionTreeRunner.loadDatasets(sc, params.input, | ||
| params.dataFormat, params.testInput, Algo.withName(params.algo), params.fracTest) | ||
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| val boostingStrategy = BoostingStrategy.defaultParams(params.algo) | ||
| boostingStrategy.numClassesForClassification = numClasses | ||
| boostingStrategy.numIterations = params.numIterations | ||
| boostingStrategy.weakLearnerParams.maxDepth = params.maxDepth | ||
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| val randomSeed = Utils.random.nextInt() | ||
| if (params.algo == "Classification") { | ||
| val startTime = System.nanoTime() | ||
| val model = GradientBoosting.trainClassifier(training, boostingStrategy) | ||
| val elapsedTime = (System.nanoTime() - startTime) / 1e9 | ||
| println(s"Training time: $elapsedTime seconds") | ||
| if (model.totalNumNodes < 30) { | ||
| println(model.toDebugString) // Print full model. | ||
| } else { | ||
| println(model) // Print model summary. | ||
| } | ||
| val trainAccuracy = | ||
| new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label))) | ||
| .precision | ||
| println(s"Train accuracy = $trainAccuracy") | ||
| val testAccuracy = | ||
| new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).precision | ||
| println(s"Test accuracy = $testAccuracy") | ||
| } else if (params.algo == "Regression") { | ||
| val startTime = System.nanoTime() | ||
| val model = GradientBoosting.trainRegressor(training, boostingStrategy) | ||
| val elapsedTime = (System.nanoTime() - startTime) / 1e9 | ||
| println(s"Training time: $elapsedTime seconds") | ||
| if (model.totalNumNodes < 30) { | ||
| println(model.toDebugString) // Print full model. | ||
| } else { | ||
| println(model) // Print model summary. | ||
| } | ||
| val trainMSE = DecisionTreeRunner.meanSquaredError(model, training) | ||
| println(s"Train mean squared error = $trainMSE") | ||
| val testMSE = DecisionTreeRunner.meanSquaredError(model, test) | ||
| println(s"Test mean squared error = $testMSE") | ||
| } | ||
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| sc.stop() | ||
| } | ||
| } |
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