Java tutorial
/** * 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.mahout.classifier.df; import java.io.IOException; import java.util.Arrays; import java.util.Random; import org.apache.commons.cli2.CommandLine; import org.apache.commons.cli2.Group; import org.apache.commons.cli2.Option; import org.apache.commons.cli2.OptionException; import org.apache.commons.cli2.builder.ArgumentBuilder; import org.apache.commons.cli2.builder.DefaultOptionBuilder; import org.apache.commons.cli2.builder.GroupBuilder; import org.apache.commons.cli2.commandline.Parser; import org.apache.commons.math3.util.FastMath; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner; import org.apache.mahout.common.CommandLineUtil; import org.apache.mahout.common.RandomUtils; import org.apache.mahout.classifier.df.builder.DefaultTreeBuilder; import org.apache.mahout.classifier.df.data.Data; import org.apache.mahout.classifier.df.data.DataLoader; import org.apache.mahout.classifier.df.data.Dataset; import org.apache.mahout.classifier.df.ref.SequentialBuilder; import org.slf4j.Logger; import org.slf4j.LoggerFactory; /** * Test procedure as described in Breiman's paper.<br> * <b>Leo Breiman: Random Forests. Machine Learning 45(1): 5-32 (2001)</b> */ public class BreimanExample extends Configured implements Tool { private static final Logger log = LoggerFactory.getLogger(BreimanExample.class); /** sum test error */ private double sumTestErrM; private double sumTestErrOne; /** mean time to build a forest with m=log2(M)+1 */ private long sumTimeM; /** mean time to build a forest with m=1 */ private long sumTimeOne; /** mean number of nodes for all the trees grown with m=log2(M)+1 */ private long numNodesM; /** mean number of nodes for all the trees grown with m=1 */ private long numNodesOne; /** * runs one iteration of the procedure. * * @param rng * random numbers generator * @param data * training data * @param m * number of random variables to select at each tree-node * @param nbtrees * number of trees to grow */ private void runIteration(Random rng, Data data, int m, int nbtrees) { log.info("Splitting the data"); Data train = data.clone(); Data test = train.rsplit(rng, (int) (data.size() * 0.1)); DefaultTreeBuilder treeBuilder = new DefaultTreeBuilder(); SequentialBuilder forestBuilder = new SequentialBuilder(rng, treeBuilder, train); // grow a forest with m = log2(M)+1 treeBuilder.setM(m); long time = System.currentTimeMillis(); log.info("Growing a forest with m={}", m); DecisionForest forestM = forestBuilder.build(nbtrees); sumTimeM += System.currentTimeMillis() - time; numNodesM += forestM.nbNodes(); // grow a forest with m=1 treeBuilder.setM(1); time = System.currentTimeMillis(); log.info("Growing a forest with m=1"); DecisionForest forestOne = forestBuilder.build(nbtrees); sumTimeOne += System.currentTimeMillis() - time; numNodesOne += forestOne.nbNodes(); // compute the test set error (Selection Error), and mean tree error (One Tree Error), double[] testLabels = test.extractLabels(); double[][] predictions = new double[test.size()][]; forestM.classify(test, predictions); double[] sumPredictions = new double[test.size()]; Arrays.fill(sumPredictions, 0.0); for (int i = 0; i < predictions.length; i++) { for (int j = 0; j < predictions[i].length; j++) { sumPredictions[i] += predictions[i][j]; } } sumTestErrM += ErrorEstimate.errorRate(testLabels, sumPredictions); forestOne.classify(test, predictions); Arrays.fill(sumPredictions, 0.0); for (int i = 0; i < predictions.length; i++) { for (int j = 0; j < predictions[i].length; j++) { sumPredictions[i] += predictions[i][j]; } } sumTestErrOne += ErrorEstimate.errorRate(testLabels, sumPredictions); } public static void main(String[] args) throws Exception { ToolRunner.run(new Configuration(), new BreimanExample(), args); } @Override public int run(String[] args) throws IOException { DefaultOptionBuilder obuilder = new DefaultOptionBuilder(); ArgumentBuilder abuilder = new ArgumentBuilder(); GroupBuilder gbuilder = new GroupBuilder(); Option dataOpt = obuilder.withLongName("data").withShortName("d").withRequired(true) .withArgument(abuilder.withName("path").withMinimum(1).withMaximum(1).create()) .withDescription("Data path").create(); Option datasetOpt = obuilder.withLongName("dataset").withShortName("ds").withRequired(true) .withArgument(abuilder.withName("dataset").withMinimum(1).withMaximum(1).create()) .withDescription("Dataset path").create(); Option nbtreesOpt = obuilder.withLongName("nbtrees").withShortName("t").withRequired(true) .withArgument(abuilder.withName("nbtrees").withMinimum(1).withMaximum(1).create()) .withDescription("Number of trees to grow, each iteration").create(); Option nbItersOpt = obuilder.withLongName("iterations").withShortName("i").withRequired(true) .withArgument(abuilder.withName("numIterations").withMinimum(1).withMaximum(1).create()) .withDescription("Number of times to repeat the test").create(); Option helpOpt = obuilder.withLongName("help").withDescription("Print out help").withShortName("h") .create(); Group group = gbuilder.withName("Options").withOption(dataOpt).withOption(datasetOpt).withOption(nbItersOpt) .withOption(nbtreesOpt).withOption(helpOpt).create(); Path dataPath; Path datasetPath; int nbTrees; int nbIterations; try { Parser parser = new Parser(); parser.setGroup(group); CommandLine cmdLine = parser.parse(args); if (cmdLine.hasOption("help")) { CommandLineUtil.printHelp(group); return -1; } String dataName = cmdLine.getValue(dataOpt).toString(); String datasetName = cmdLine.getValue(datasetOpt).toString(); nbTrees = Integer.parseInt(cmdLine.getValue(nbtreesOpt).toString()); nbIterations = Integer.parseInt(cmdLine.getValue(nbItersOpt).toString()); dataPath = new Path(dataName); datasetPath = new Path(datasetName); } catch (OptionException e) { log.error("Error while parsing options", e); CommandLineUtil.printHelp(group); return -1; } // load the data FileSystem fs = dataPath.getFileSystem(new Configuration()); Dataset dataset = Dataset.load(getConf(), datasetPath); Data data = DataLoader.loadData(dataset, fs, dataPath); // take m to be the first integer less than log2(M) + 1, where M is the // number of inputs int m = (int) Math.floor(FastMath.log(2.0, data.getDataset().nbAttributes()) + 1); Random rng = RandomUtils.getRandom(); for (int iteration = 0; iteration < nbIterations; iteration++) { log.info("Iteration {}", iteration); runIteration(rng, data, m, nbTrees); } log.info("********************************************"); log.info("Random Input Test Error : {}", sumTestErrM / nbIterations); log.info("Single Input Test Error : {}", sumTestErrOne / nbIterations); log.info("Mean Random Input Time : {}", DFUtils.elapsedTime(sumTimeM / nbIterations)); log.info("Mean Single Input Time : {}", DFUtils.elapsedTime(sumTimeOne / nbIterations)); log.info("Mean Random Input Num Nodes : {}", numNodesM / nbIterations); log.info("Mean Single Input Num Nodes : {}", numNodesOne / nbIterations); return 0; } }