/*
* 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.mahout.classifier.df.mapreduce;
import java.io.IOException;
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 {
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);
if (forest == null) {
return;
}
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);
}
}