/**
* 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.clustering.syntheticcontrol.meanshift;
import java.util.List;
import java.util.Map;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.util.ToolRunner;
import org.apache.mahout.clustering.kernel.IKernelProfile;
import org.apache.mahout.clustering.conversion.meanshift.InputDriver;
import org.apache.mahout.clustering.meanshift.MeanShiftCanopyDriver;
import org.apache.mahout.common.AbstractJob;
import org.apache.mahout.common.ClassUtils;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.commandline.DefaultOptionCreator;
import org.apache.mahout.common.distance.DistanceMeasure;
import org.apache.mahout.common.distance.EuclideanDistanceMeasure;
import org.apache.mahout.clustering.kernel.TriangularKernelProfile;
import org.apache.mahout.utils.clustering.ClusterDumper;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public final class Job extends AbstractJob {
private static final Logger log = LoggerFactory.getLogger(Job.class);
private static final String DIRECTORY_CONTAINING_CONVERTED_INPUT = "data";
private Job() {
}
public static void main(String[] args) throws Exception {
if (args.length > 0) {
log.info("Running with only user-supplied arguments");
ToolRunner.run(new Configuration(), new Job(), args);
} else {
log.info("Running with default arguments");
Path output = new Path("output");
Configuration conf = new Configuration();
HadoopUtil.delete(conf, output);
run(conf, new Path("testdata"), output, new EuclideanDistanceMeasure(), new TriangularKernelProfile(), 47.6, 1,
0.5, 10);
}
}
@Override
public int run(String[] args) throws Exception {
addInputOption();
addOutputOption();
addOption(DefaultOptionCreator.convergenceOption().create());
addOption(DefaultOptionCreator.maxIterationsOption().create());
addOption(DefaultOptionCreator.overwriteOption().create());
addOption(DefaultOptionCreator.inputIsCanopiesOption().create());
addOption(DefaultOptionCreator.distanceMeasureOption().create());
addOption(DefaultOptionCreator.kernelProfileOption().create());
addOption(DefaultOptionCreator.t1Option().create());
addOption(DefaultOptionCreator.t2Option().create());
addOption(DefaultOptionCreator.clusteringOption().create());
Map<String,List<String>> argMap = parseArguments(args);
if (argMap == null) {
return -1;
}
Path input = getInputPath();
Path output = getOutputPath();
if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) {
HadoopUtil.delete(new Configuration(), output);
}
String measureClass = getOption(DefaultOptionCreator.DISTANCE_MEASURE_OPTION);
String kernelProfileClass = getOption(DefaultOptionCreator.KERNEL_PROFILE_OPTION);
double t1 = Double.parseDouble(getOption(DefaultOptionCreator.T1_OPTION));
double t2 = Double.parseDouble(getOption(DefaultOptionCreator.T2_OPTION));
double convergenceDelta = Double.parseDouble(getOption(DefaultOptionCreator.CONVERGENCE_DELTA_OPTION));
int maxIterations = Integer.parseInt(getOption(DefaultOptionCreator.MAX_ITERATIONS_OPTION));
DistanceMeasure measure = ClassUtils.instantiateAs(measureClass, DistanceMeasure.class);
IKernelProfile kernelProfile = ClassUtils.instantiateAs(kernelProfileClass, IKernelProfile.class);
run(getConf(), input, output, measure, kernelProfile, t1, t2, convergenceDelta, maxIterations);
return 0;
}
/**
* Run the meanshift clustering job on an input dataset using the given
* distance measure, t1, t2 and iteration parameters. All output data will be
* written to the output directory, which will be initially deleted if it
* exists. The clustered points will reside in the path
* <output>/clustered-points. By default, the job expects the a file
* containing synthetic_control.data as obtained from
* http://archive.ics.uci.edu/ml/datasets/Synthetic+Control+Chart+Time+Series
* resides in a directory named "testdata", and writes output to a directory
* named "output".
*
* @param input
* the String denoting the input directory path
* @param output
* the String denoting the output directory path
* @param measure
* the DistanceMeasure to use
* @param kernelProfile
* the IKernelProfile to use
* @param t1
* the meanshift canopy T1 threshold
* @param t2
* the meanshift canopy T2 threshold
* @param convergenceDelta
* the double convergence criteria for iterations
* @param maxIterations
* the int maximum number of iterations
*/
public static void run(Configuration conf,
Path input,
Path output,
DistanceMeasure measure,
IKernelProfile kernelProfile,
double t1,
double t2,
double convergenceDelta,
int maxIterations)
throws Exception {
Path directoryContainingConvertedInput = new Path(output,
DIRECTORY_CONTAINING_CONVERTED_INPUT);
InputDriver.runJob(input, directoryContainingConvertedInput);
MeanShiftCanopyDriver.run(conf, directoryContainingConvertedInput, output,
measure, kernelProfile, t1, t2, convergenceDelta, maxIterations, true,
true, false);
// run ClusterDumper
ClusterDumper clusterDumper = new ClusterDumper(new Path(output,
"clusters-*-final"), new Path(output, "clusteredPoints"));
clusterDumper.printClusters(null);
}
}