Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/* 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.kmeans;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.util.ToolRunner;
import org.apache.mahout.clustering.Cluster;
import org.apache.mahout.clustering.classify.ClusterClassificationDriver;
import org.apache.mahout.clustering.classify.ClusterClassifier;
import org.apache.mahout.clustering.iterator.ClusterIterator;
import org.apache.mahout.clustering.iterator.ClusteringPolicy;
import org.apache.mahout.clustering.iterator.KMeansClusteringPolicy;
import org.apache.mahout.clustering.topdown.PathDirectory;
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.SquaredEuclideanDistanceMeasure;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public class KMeansDriver extends AbstractJob {
private static final Logger log = LoggerFactory.getLogger(KMeansDriver.class);
public static void main(String[] args) throws Exception {
ToolRunner.run(new Configuration(), new KMeansDriver(), args);
}
@Override
public int run(String[] args) throws Exception {
addInputOption();
addOutputOption();
addOption(DefaultOptionCreator.distanceMeasureOption().create());
addOption(DefaultOptionCreator
.clustersInOption()
.withDescription(
"The input centroids, as Vectors. Must be a SequenceFile of Writable, Cluster/Canopy. "
+ "If k is also specified, then a random set of vectors will be selected"
+ " and written out to this path first").create());
addOption(DefaultOptionCreator
.numClustersOption()
.withDescription(
"The k in k-Means. If specified, then a random selection of k Vectors will be chosen"
+ " as the Centroid and written to the clusters input path.").create());
addOption(DefaultOptionCreator.useSetRandomSeedOption().create());
addOption(DefaultOptionCreator.convergenceOption().create());
addOption(DefaultOptionCreator.maxIterationsOption().create());
addOption(DefaultOptionCreator.overwriteOption().create());
addOption(DefaultOptionCreator.clusteringOption().create());
addOption(DefaultOptionCreator.methodOption().create());
addOption(DefaultOptionCreator.outlierThresholdOption().create());
if (parseArguments(args) == null) {
return -1;
}
Path input = getInputPath();
Path clusters = new Path(getOption(DefaultOptionCreator.CLUSTERS_IN_OPTION));
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);
}
DistanceMeasure measure = ClassUtils.instantiateAs(measureClass, DistanceMeasure.class);
if (hasOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION)) {
int numClusters = Integer.parseInt(getOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION));
Long seed = null;
if (hasOption(DefaultOptionCreator.RANDOM_SEED)) {
seed = Long.parseLong(getOption(DefaultOptionCreator.RANDOM_SEED));
}
clusters = RandomSeedGenerator.buildRandom(getConf(), input, clusters, numClusters, measure, seed);
}
boolean runClustering = hasOption(DefaultOptionCreator.CLUSTERING_OPTION);
boolean runSequential = getOption(DefaultOptionCreator.METHOD_OPTION).equalsIgnoreCase(
DefaultOptionCreator.SEQUENTIAL_METHOD);
double clusterClassificationThreshold = 0.0;
if (hasOption(DefaultOptionCreator.OUTLIER_THRESHOLD)) {
clusterClassificationThreshold = Double.parseDouble(getOption(DefaultOptionCreator.OUTLIER_THRESHOLD));
}
run(getConf(), input, clusters, output, convergenceDelta, maxIterations, runClustering,
clusterClassificationThreshold, runSequential);
return 0;
}
/**
* Iterate over the input vectors to produce clusters and, if requested, use the results of the final iteration to
* cluster the input vectors.
*
* @param input
* the directory pathname for input points
* @param clustersIn
* the directory pathname for initial & computed clusters
* @param output
* the directory pathname for output points
* @param convergenceDelta
* the convergence delta value
* @param maxIterations
* the maximum number of iterations
* @param runClustering
* true if points are to be clustered after iterations are completed
* @param clusterClassificationThreshold
* Is a clustering strictness / outlier removal parameter. Its value should be between 0 and 1. Vectors
* having pdf below this value will not be clustered.
* @param runSequential
* if true execute sequential algorithm
*/
public static void run(Configuration conf, Path input, Path clustersIn, Path output,
double convergenceDelta, int maxIterations, boolean runClustering, double clusterClassificationThreshold,
boolean runSequential) throws IOException, InterruptedException, ClassNotFoundException {
// iterate until the clusters converge
String delta = Double.toString(convergenceDelta);
if (log.isInfoEnabled()) {
log.info("Input: {} Clusters In: {} Out: {}", input, clustersIn, output);
log.info("convergence: {} max Iterations: {}", convergenceDelta, maxIterations);
}
Path clustersOut = buildClusters(conf, input, clustersIn, output, maxIterations, delta, runSequential);
if (runClustering) {
log.info("Clustering data");
clusterData(conf, input, clustersOut, output, clusterClassificationThreshold, runSequential);
}
}
/**
* Iterate over the input vectors to produce clusters and, if requested, use the results of the final iteration to
* cluster the input vectors.
*
* @param input
* the directory pathname for input points
* @param clustersIn
* the directory pathname for initial & computed clusters
* @param output
* the directory pathname for output points
* @param convergenceDelta
* the convergence delta value
* @param maxIterations
* the maximum number of iterations
* @param runClustering
* true if points are to be clustered after iterations are completed
* @param clusterClassificationThreshold
* Is a clustering strictness / outlier removal parameter. Its value should be between 0 and 1. Vectors
* having pdf below this value will not be clustered.
* @param runSequential
* if true execute sequential algorithm
*/
public static void run(Path input, Path clustersIn, Path output, double convergenceDelta,
int maxIterations, boolean runClustering, double clusterClassificationThreshold, boolean runSequential)
throws IOException, InterruptedException, ClassNotFoundException {
run(new Configuration(), input, clustersIn, output, convergenceDelta, maxIterations, runClustering,
clusterClassificationThreshold, runSequential);
}
/**
* Iterate over the input vectors to produce cluster directories for each iteration
*
*
* @param conf
* the Configuration to use
* @param input
* the directory pathname for input points
* @param clustersIn
* the directory pathname for initial & computed clusters
* @param output
* the directory pathname for output points
* @param maxIterations
* the maximum number of iterations
* @param delta
* the convergence delta value
* @param runSequential
* if true execute sequential algorithm
*
* @return the Path of the final clusters directory
*/
public static Path buildClusters(Configuration conf, Path input, Path clustersIn, Path output,
int maxIterations, String delta, boolean runSequential) throws IOException,
InterruptedException, ClassNotFoundException {
double convergenceDelta = Double.parseDouble(delta);
List clusters = new ArrayList<>();
KMeansUtil.configureWithClusterInfo(conf, clustersIn, clusters);
if (clusters.isEmpty()) {
throw new IllegalStateException("No input clusters found in " + clustersIn + ". Check your -c argument.");
}
Path priorClustersPath = new Path(output, Cluster.INITIAL_CLUSTERS_DIR);
ClusteringPolicy policy = new KMeansClusteringPolicy(convergenceDelta);
ClusterClassifier prior = new ClusterClassifier(clusters, policy);
prior.writeToSeqFiles(priorClustersPath);
if (runSequential) {
ClusterIterator.iterateSeq(conf, input, priorClustersPath, output, maxIterations);
} else {
ClusterIterator.iterateMR(conf, input, priorClustersPath, output, maxIterations);
}
return output;
}
/**
* Run the job using supplied arguments
*
* @param input
* the directory pathname for input points
* @param clustersIn
* the directory pathname for input clusters
* @param output
* the directory pathname for output points
* @param clusterClassificationThreshold
* Is a clustering strictness / outlier removal parameter. Its value should be between 0 and 1. Vectors
* having pdf below this value will not be clustered.
* @param runSequential
* if true execute sequential algorithm
*/
public static void clusterData(Configuration conf, Path input, Path clustersIn, Path output,
double clusterClassificationThreshold, boolean runSequential) throws IOException, InterruptedException,
ClassNotFoundException {
if (log.isInfoEnabled()) {
log.info("Running Clustering");
log.info("Input: {} Clusters In: {} Out: {}", input, clustersIn, output);
}
ClusterClassifier.writePolicy(new KMeansClusteringPolicy(), clustersIn);
ClusterClassificationDriver.run(conf, input, output, new Path(output, PathDirectory.CLUSTERED_POINTS_DIRECTORY),
clusterClassificationThreshold, true, runSequential);
}
}
