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.ga.watchmaker.cd; import java.io.IOException; import java.util.List; import com.google.common.collect.Lists; 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.hadoop.fs.Path; import org.apache.mahout.common.CommandLineUtil; import org.apache.mahout.common.RandomUtils; import org.apache.mahout.common.commandline.DefaultOptionCreator; import org.apache.mahout.ga.watchmaker.cd.hadoop.CDMahoutEvaluator; import org.apache.mahout.ga.watchmaker.cd.hadoop.DatasetSplit; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.uncommons.watchmaker.framework.CandidateFactory; import org.uncommons.watchmaker.framework.EvolutionEngine; import org.uncommons.watchmaker.framework.EvolutionObserver; import org.uncommons.watchmaker.framework.EvolutionaryOperator; import org.uncommons.watchmaker.framework.FitnessEvaluator; import org.uncommons.watchmaker.framework.PopulationData; import org.uncommons.watchmaker.framework.SelectionStrategy; import org.uncommons.watchmaker.framework.SequentialEvolutionEngine; import org.uncommons.watchmaker.framework.operators.EvolutionPipeline; import org.uncommons.watchmaker.framework.selection.RouletteWheelSelection; import org.uncommons.watchmaker.framework.termination.GenerationCount; /** * Class Discovery Genetic Algorithm main class. Has the following parameters: * <ul> * <li>threshold<br> * Condition activation threshold. See Also {@link org.apache.mahout.ga.watchmaker.cd.CDRule CDRule} * <li>nb cross point<br> * Number of points used by the{@link org.apache.mahout.ga.watchmaker.cd.CDCrossover CrossOver} operator * <li>mutation rate<br> * mutation rate of the {@link org.apache.mahout.ga.watchmaker.cd.CDMutation Mutation} operator * <li>mutation range<br> * mutation range of the {@link org.apache.mahout.ga.watchmaker.cd.CDMutation Mutation} operator * <li>mutation precision<br> * mutation precision of the {@link org.apache.mahout.ga.watchmaker.cd.CDMutation Mutation} operator * <li>population size * <li>generations count<br> * number of generations the genetic algorithm will be run for. * * </ul> */ public final class CDGA { private static final Logger log = LoggerFactory.getLogger(CDGA.class); private CDGA() { } public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException { DefaultOptionBuilder obuilder = new DefaultOptionBuilder(); ArgumentBuilder abuilder = new ArgumentBuilder(); GroupBuilder gbuilder = new GroupBuilder(); Option inputOpt = DefaultOptionCreator.inputOption().create(); Option labelOpt = obuilder.withLongName("label").withRequired(true).withShortName("l") .withArgument(abuilder.withName("index").withMinimum(1).withMaximum(1).create()) .withDescription("label's index.").create(); Option thresholdOpt = obuilder.withLongName("threshold").withRequired(false).withShortName("t") .withArgument(abuilder.withName("threshold").withMinimum(1).withMaximum(1).create()) .withDescription("Condition activation threshold, default = 0.5.").create(); Option crosspntsOpt = obuilder.withLongName("crosspnts").withRequired(false).withShortName("cp") .withArgument(abuilder.withName("points").withMinimum(1).withMaximum(1).create()) .withDescription("Number of crossover points to use, default = 1.").create(); Option mutrateOpt = obuilder.withLongName("mutrate").withRequired(true).withShortName("m") .withArgument(abuilder.withName("true").withMinimum(1).withMaximum(1).create()) .withDescription("Mutation rate (float).").create(); Option mutrangeOpt = obuilder.withLongName("mutrange").withRequired(false).withShortName("mr") .withArgument(abuilder.withName("range").withMinimum(1).withMaximum(1).create()) .withDescription("Mutation range, default = 0.1 (10%).").create(); Option mutprecOpt = obuilder.withLongName("mutprec").withRequired(false).withShortName("mp") .withArgument(abuilder.withName("precision").withMinimum(1).withMaximum(1).create()) .withDescription("Mutation precision, default = 2.").create(); Option popsizeOpt = obuilder.withLongName("popsize").withRequired(true).withShortName("p") .withArgument(abuilder.withName("size").withMinimum(1).withMaximum(1).create()) .withDescription("Population size.").create(); Option gencntOpt = obuilder.withLongName("gencnt").withRequired(true).withShortName("g") .withArgument(abuilder.withName("count").withMinimum(1).withMaximum(1).create()) .withDescription("Generations count.").create(); Option helpOpt = DefaultOptionCreator.helpOption(); Group group = gbuilder.withName("Options").withOption(inputOpt).withOption(helpOpt).withOption(labelOpt) .withOption(thresholdOpt).withOption(crosspntsOpt).withOption(mutrateOpt).withOption(mutrangeOpt) .withOption(mutprecOpt).withOption(popsizeOpt).withOption(gencntOpt).create(); Parser parser = new Parser(); parser.setGroup(group); try { CommandLine cmdLine = parser.parse(args); if (cmdLine.hasOption(helpOpt)) { CommandLineUtil.printHelp(group); return; } String dataset = cmdLine.getValue(inputOpt).toString(); int target = Integer.parseInt(cmdLine.getValue(labelOpt).toString()); double threshold = cmdLine.hasOption(thresholdOpt) ? Double.parseDouble(cmdLine.getValue(thresholdOpt).toString()) : 0.5; int crosspnts = cmdLine.hasOption(crosspntsOpt) ? Integer.parseInt(cmdLine.getValue(crosspntsOpt).toString()) : 1; double mutrate = Double.parseDouble(cmdLine.getValue(mutrateOpt).toString()); double mutrange = cmdLine.hasOption(mutrangeOpt) ? Double.parseDouble(cmdLine.getValue(mutrangeOpt).toString()) : 0.1; int mutprec = cmdLine.hasOption(mutprecOpt) ? Integer.parseInt(cmdLine.getValue(mutprecOpt).toString()) : 2; int popSize = Integer.parseInt(cmdLine.getValue(popsizeOpt).toString()); int genCount = Integer.parseInt(cmdLine.getValue(gencntOpt).toString()); long start = System.currentTimeMillis(); runJob(dataset, target, threshold, crosspnts, mutrate, mutrange, mutprec, popSize, genCount); long end = System.currentTimeMillis(); printElapsedTime(end - start); } catch (OptionException e) { log.error("Error while parsing options", e); CommandLineUtil.printHelp(group); } } private static void runJob(String dataset, int target, double threshold, int crosspnts, double mutrate, double mutrange, int mutprec, int popSize, int genCount) throws IOException, InterruptedException, ClassNotFoundException { Path inpath = new Path(dataset); CDMahoutEvaluator.initializeDataSet(inpath); // Candidate Factory CandidateFactory<CDRule> factory = new CDFactory(threshold); // Evolution Scheme List<EvolutionaryOperator<CDRule>> operators = Lists.newArrayList(); operators.add(new CDCrossover(crosspnts)); operators.add(new CDMutation(mutrate, mutrange, mutprec)); EvolutionPipeline<CDRule> pipeline = new EvolutionPipeline<CDRule>(operators); // 75 % of the dataset is dedicated to training DatasetSplit split = new DatasetSplit(0.75); // Fitness Evaluator (defaults to training) FitnessEvaluator<? super CDRule> evaluator = new CDFitnessEvaluator(dataset, target, split); // Selection Strategy SelectionStrategy<? super CDRule> selection = new RouletteWheelSelection(); EvolutionEngine<CDRule> engine = new SequentialEvolutionEngine<CDRule>(factory, pipeline, evaluator, selection, RandomUtils.getRandom()); engine.addEvolutionObserver(new EvolutionObserver<CDRule>() { @Override public void populationUpdate(PopulationData<? extends CDRule> data) { log.info("Generation {}", data.getGenerationNumber()); } }); // evolve the rules over the training set Rule solution = engine.evolve(popSize, 1, new GenerationCount(genCount)); Path output = new Path("output"); // fitness over the training set CDFitness bestTrainFit = CDMahoutEvaluator.evaluate(solution, target, inpath, output, split); // fitness over the testing set split.setTraining(false); CDFitness bestTestFit = CDMahoutEvaluator.evaluate(solution, target, inpath, output, split); // evaluate the solution over the testing set log.info("Best solution fitness (train set) : {}", bestTrainFit); log.info("Best solution fitness (test set) : {}", bestTestFit); } private static void printElapsedTime(long milli) { long seconds = milli / 1000; milli %= 1000; long minutes = seconds / 60; seconds %= 60; long hours = minutes / 60; minutes %= 60; log.info("Elapsed time (Hours:minutes:seconds:milli) : {}:{}:{}:{}", new Object[] { hours, minutes, seconds, milli }); } }