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 bigimp; import java.io.IOException; import org.apache.mahout.classifier.df.mapreduce.Builder; 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.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.classifier.df.DFUtils; import org.apache.mahout.classifier.df.DecisionForest; import org.apache.mahout.classifier.df.builder.DecisionTreeBuilder; 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.mapreduce.inmem.InMemBuilder; import org.apache.mahout.classifier.df.mapreduce.partial.PartialBuilder; import org.slf4j.Logger; import org.slf4j.LoggerFactory; /** * Tool to builds a Random Forest using any given dataset (in UCI format). Can use either the in-mem mapred or * partial mapred implementations. Stores the forest in the given output directory */ public class BuildForest extends Configured implements Tool { private static final Logger log = LoggerFactory.getLogger(BuildForest.class); private Path dataPath; private Path datasetPath; private Path outputPath; private Integer m; // Number of variables to select at each tree-node private boolean complemented; // tree is complemented private Integer minSplitNum; // minimum number for split private Double minVarianceProportion; // minimum proportion of the total variance for split private int nbTrees; // Number of trees to grow private Long seed; // Random seed private boolean isPartial; // use partial data implementation @Override public int run(String[] args) throws IOException, ClassNotFoundException, InterruptedException, InstantiationException, IllegalAccessException { 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 selectionOpt = obuilder.withLongName("selection").withShortName("sl").withRequired(false) .withArgument(abuilder.withName("m").withMinimum(1).withMaximum(1).create()) .withDescription("Optional, Number of variables to select randomly at each tree-node.\n" + "For classification problem, the default is square root of the number of explanatory variables.\n" + "For regression problem, the default is 1/3 of the number of explanatory variables.") .create(); Option noCompleteOpt = obuilder.withLongName("no-complete").withShortName("nc").withRequired(false) .withDescription("Optional, The tree is not complemented").create(); Option minSplitOpt = obuilder.withLongName("minsplit").withShortName("ms").withRequired(false) .withArgument(abuilder.withName("minsplit").withMinimum(1).withMaximum(1).create()) .withDescription("Optional, The tree-node is not divided, if the branching data size is " + "smaller than this value.\nThe default is 2.") .create(); Option minPropOpt = obuilder.withLongName("minprop").withShortName("mp").withRequired(false) .withArgument(abuilder.withName("minprop").withMinimum(1).withMaximum(1).create()) .withDescription("Optional, The tree-node is not divided, if the proportion of the " + "variance of branching data is smaller than this value.\n" + "In the case of a regression problem, this value is used. " + "The default is 1/1000(0.001).") .create(); Option seedOpt = obuilder.withLongName("seed").withShortName("sd").withRequired(false) .withArgument(abuilder.withName("seed").withMinimum(1).withMaximum(1).create()) .withDescription("Optional, seed value used to initialise the Random number generator").create(); Option partialOpt = obuilder.withLongName("partial").withShortName("p").withRequired(false) .withDescription("Optional, use the Partial Data implementation").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").create(); Option outputOpt = obuilder.withLongName("output").withShortName("o").withRequired(true) .withArgument(abuilder.withName("path").withMinimum(1).withMaximum(1).create()) .withDescription("Output path, will contain the Decision Forest").create(); Option helpOpt = obuilder.withLongName("help").withShortName("h").withDescription("Print out help") .create(); Group group = gbuilder.withName("Options").withOption(dataOpt).withOption(datasetOpt) .withOption(selectionOpt).withOption(noCompleteOpt).withOption(minSplitOpt).withOption(minPropOpt) .withOption(seedOpt).withOption(partialOpt).withOption(nbtreesOpt).withOption(outputOpt) .withOption(helpOpt).create(); try { Parser parser = new Parser(); parser.setGroup(group); CommandLine cmdLine = parser.parse(args); if (cmdLine.hasOption("help")) { CommandLineUtil.printHelp(group); return -1; } isPartial = cmdLine.hasOption(partialOpt); String dataName = cmdLine.getValue(dataOpt).toString(); String datasetName = cmdLine.getValue(datasetOpt).toString(); String outputName = cmdLine.getValue(outputOpt).toString(); nbTrees = Integer.parseInt(cmdLine.getValue(nbtreesOpt).toString()); if (cmdLine.hasOption(selectionOpt)) { m = Integer.parseInt(cmdLine.getValue(selectionOpt).toString()); } complemented = !cmdLine.hasOption(noCompleteOpt); if (cmdLine.hasOption(minSplitOpt)) { minSplitNum = Integer.parseInt(cmdLine.getValue(minSplitOpt).toString()); } if (cmdLine.hasOption(minPropOpt)) { minVarianceProportion = Double.parseDouble(cmdLine.getValue(minPropOpt).toString()); } if (cmdLine.hasOption(seedOpt)) { seed = Long.valueOf(cmdLine.getValue(seedOpt).toString()); } if (log.isDebugEnabled()) { log.debug("data : {}", dataName); log.debug("dataset : {}", datasetName); log.debug("output : {}", outputName); log.debug("m : {}", m); log.debug("complemented : {}", complemented); log.debug("minSplitNum : {}", minSplitNum); log.debug("minVarianceProportion : {}", minVarianceProportion); log.debug("seed : {}", seed); log.debug("nbtrees : {}", nbTrees); log.debug("isPartial : {}", isPartial); } dataPath = new Path(dataName); datasetPath = new Path(datasetName); outputPath = new Path(outputName); } catch (OptionException e) { log.error("Exception", e); CommandLineUtil.printHelp(group); return -1; } buildForest(); return 0; } private void buildForest() throws IOException, ClassNotFoundException, InterruptedException { // make sure the output path does not exist FileSystem ofs = outputPath.getFileSystem(getConf()); if (ofs.exists(outputPath)) { log.error("Output path already exists"); return; } DecisionTreeBuilder treeBuilder = new DecisionTreeBuilder(); if (m != null) { treeBuilder.setM(m); } treeBuilder.setComplemented(complemented); if (minSplitNum != null) { treeBuilder.setMinSplitNum(minSplitNum); } if (minVarianceProportion != null) { treeBuilder.setMinVarianceProportion(minVarianceProportion); } Builder forestBuilder; if (isPartial) { log.info("Partial Mapred implementation"); forestBuilder = new PartialBuilder(treeBuilder, dataPath, datasetPath, seed, getConf()); } else { log.info("InMem Mapred implementation"); forestBuilder = new InMemBuilder(treeBuilder, dataPath, datasetPath, seed, getConf()); } forestBuilder.setOutputDirName(outputPath.getName()); log.info("Building the forest..."); long time = System.currentTimeMillis(); DecisionForest forest = forestBuilder.build(nbTrees); time = System.currentTimeMillis() - time; log.info("Build Time: {}", DFUtils.elapsedTime(time)); log.info("Forest num Nodes: {}", forest.nbNodes()); log.info("Forest mean num Nodes: {}", forest.meanNbNodes()); log.info("Forest mean max Depth: {}", forest.meanMaxDepth()); // store the decision forest in the output path Path forestPath = new Path(outputPath, "forest.seq"); log.info("Storing the forest in: {}", forestPath); DFUtils.storeWritable(getConf(), forestPath, forest); } protected static Data loadData(Configuration conf, Path dataPath, Dataset dataset) throws IOException { log.info("Loading the data..."); FileSystem fs = dataPath.getFileSystem(conf); Data data = DataLoader.loadData(dataset, fs, dataPath); log.info("Data Loaded"); return data; } public static void main(String[] args) throws Exception { ToolRunner.run(new Configuration(), new BuildForest(), args); } }