package org.apache.mahout.clustering.syntheticcontrol.kmeans;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.util.ToolRunner;
import org.apache.mahout.clustering.Cluster;
import org.apache.mahout.clustering.canopy.CanopyDriver;
import org.apache.mahout.clustering.conversion.InputDriver;
import org.apache.mahout.clustering.kmeans.KMeansDriver;
import org.apache.mahout.clustering.kmeans.RandomSeedGenerator;
import org.apache.mahout.common.AbstractJob;
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.common.distance.SquaredEuclideanDistanceMeasure;
import org.apache.mahout.utils.clustering.ClusterDumper;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.IOException;
import java.util.Map;
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);
new Job().run(conf, new Path("testdata"), output,
new EuclideanDistanceMeasure(), 6, 0.5, 10);
}
}
@Override
public int run(String[] args)
throws IOException, ClassNotFoundException, InstantiationException,
IllegalAccessException, InterruptedException {
addInputOption();
addOutputOption();
addOption(DefaultOptionCreator.distanceMeasureOption().create());
addOption(DefaultOptionCreator.numClustersOption().create());
addOption(DefaultOptionCreator.t1Option().create());
addOption(DefaultOptionCreator.t2Option().create());
addOption(DefaultOptionCreator.convergenceOption().create());
addOption(DefaultOptionCreator.maxIterationsOption().create());
addOption(DefaultOptionCreator.overwriteOption().create());
Map<String, String> argMap = parseArguments(args);
if (argMap == null) {
return -1;
}
Path input = getInputPath();
Path output = getOutputPath();
String measureClass = getOption(DefaultOptionCreator.DISTANCE_MEASURE_OPTION);
if (measureClass == null) {
measureClass = SquaredEuclideanDistanceMeasure.class.getName();
}
double convergenceDelta = Double.parseDouble(getOption(
DefaultOptionCreator.CONVERGENCE_DELTA_OPTION));
int maxIterations = Integer.parseInt(getOption(
DefaultOptionCreator.MAX_ITERATIONS_OPTION));
if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) {
HadoopUtil.delete(getConf(), output);
}
ClassLoader ccl = Thread.currentThread().getContextClassLoader();
Class<?> cl = ccl.loadClass(measureClass);
DistanceMeasure measure = (DistanceMeasure) cl.newInstance();
if (hasOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION)) {
int k = Integer.parseInt(getOption(
DefaultOptionCreator.NUM_CLUSTERS_OPTION));
run(getConf(), input, output, measure, k, convergenceDelta,
maxIterations);
} else {
double t1 = Double.parseDouble(getOption(
DefaultOptionCreator.T1_OPTION));
double t2 = Double.parseDouble(getOption(
DefaultOptionCreator.T2_OPTION));
run(getConf(), input, output, measure, t1, t2, convergenceDelta,
maxIterations);
}
return 0;
}
/**
* Run the kmeans clustering job on an input dataset using the given the
* number of clusters k 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 a file containing
* equal length space delimited data that resides in a directory named
* "testdata", and writes output to a directory named "output".
*
* @param conf
* the Configuration to use
* @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 k
* the number of clusters in Kmeans
* @param convergenceDelta
* the double convergence criteria for iterations
* @param maxIterations
* the int maximum number of iterations
*/
public void run(Configuration conf, Path input, Path output,
DistanceMeasure measure, int k, double convergenceDelta,
int maxIterations)
throws IOException, InterruptedException, ClassNotFoundException {
Path directoryContainingConvertedInput = new Path(output,
DIRECTORY_CONTAINING_CONVERTED_INPUT);
log.info("Preparing Input");
InputDriver.runJob(input, directoryContainingConvertedInput,
"org.apache.mahout.math.RandomAccessSparseVector");
log.info("Running random seed to get initial clusters");
Path clusters = new Path(output, Cluster.INITIAL_CLUSTERS_DIR);
clusters = RandomSeedGenerator.buildRandom(conf,
directoryContainingConvertedInput, clusters, k, measure);
log.info("Running KMeans");
KMeansDriver.run(conf, directoryContainingConvertedInput, clusters,
output, measure, convergenceDelta, maxIterations, true, false);
ClusterDumper clusterDumper = new ClusterDumper(finalClusterPath(conf,
output, maxIterations), new Path(output, "clusteredPoints"));
clusterDumper.printClusters(null);
}
/**
* Run the kmeans 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
* resides in a directory named "testdata", and writes output to a directory
* named "output".
*
* @param conf
* the Configuration to use
* @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 t1
* the canopy T1 threshold
* @param t2
* the canopy T2 threshold
* @param convergenceDelta
* the double convergence criteria for iterations
* @param maxIterations
* the int maximum number of iterations
* @throws IOException
* @throws InterruptedException
* @throws ClassNotFoundException
* @throws IllegalAccessException
* @throws InstantiationException
*/
public void run(Configuration conf, Path input, Path output,
DistanceMeasure measure, double t1, double t2, double convergenceDelta,
int maxIterations)
throws IOException, InterruptedException, ClassNotFoundException,
InstantiationException, IllegalAccessException {
Path directoryContainingConvertedInput = new Path(output,
DIRECTORY_CONTAINING_CONVERTED_INPUT);
log.info("Preparing Input");
InputDriver.runJob(input, directoryContainingConvertedInput,
"org.apache.mahout.math.RandomAccessSparseVector");
log.info("Running Canopy to get initial clusters");
CanopyDriver.run(conf, directoryContainingConvertedInput, output,
measure, t1, t2, false, false);
log.info("Running KMeans");
KMeansDriver.run(conf, directoryContainingConvertedInput,
new Path(output, Cluster.INITIAL_CLUSTERS_DIR), output, measure,
convergenceDelta, maxIterations, true, false);
ClusterDumper clusterDumper = new ClusterDumper(finalClusterPath(conf,
output, maxIterations), new Path(output, "clusteredPoints"));
clusterDumper.printClusters(null);
}
/**
* Return the path to the final iteration's clusters
*/
private static Path finalClusterPath(Configuration conf, Path output,
int maxIterations) throws IOException {
FileSystem fs = FileSystem.get(conf);
for (int i = maxIterations; i >= 0; i--) {
Path clusters = new Path(output, "clusters-" + i);
if (fs.exists(clusters)) {
return clusters;
}
}
return null;
}
}