/**
* 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.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.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;
import org.uncommons.maths.Maths;
/**
* 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);
sumTestErrM += ErrorEstimate.errorRate(testLabels, predictions);
forestOne.classify(test, predictions);
sumTestErrOne += ErrorEstimate.errorRate(testLabels, predictions);
}
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(Maths.log(2, 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;
}
}