/**
* 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.Collection;
import com.google.common.collect.Lists;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.mahout.clustering.WeightedPropertyVectorWritable;
import org.apache.mahout.common.ClassUtils;
import org.apache.mahout.common.distance.DistanceMeasure;
import org.apache.mahout.math.VectorWritable;
/**
* The {@link org.apache.mahout.clustering.kmeans.KMeansClusterMapper} is responsible for calculating
* which points belong to which clusters and outputting the information. This is an optional step,
* as some applications only care about what the Centroids are.
*
* @see KMeansDriver for more information on how to invoke this process
*/
public class KMeansClusterMapper
extends Mapper<WritableComparable<?>,VectorWritable,IntWritable,WeightedPropertyVectorWritable> {
private final Collection<Cluster> clusters = Lists.newArrayList();
private KMeansClusterer clusterer;
@Override
protected void map(WritableComparable<?> key, VectorWritable point, Context context)
throws IOException, InterruptedException {
clusterer.outputPointWithClusterInfo(point.get(), clusters, context);
}
@Override
protected void setup(Context context) throws IOException, InterruptedException {
super.setup(context);
Configuration conf = context.getConfiguration();
DistanceMeasure measure =
ClassUtils.instantiateAs(conf.get(KMeansConfigKeys.DISTANCE_MEASURE_KEY), DistanceMeasure.class);
measure.configure(conf);
String clusterPath = conf.get(KMeansConfigKeys.CLUSTER_PATH_KEY);
if (clusterPath != null && !clusterPath.isEmpty()) {
KMeansUtil.configureWithClusterInfo(conf, new Path(clusterPath), clusters);
if (clusters.isEmpty()) {
throw new IllegalStateException("No clusters found. Check your -c path.");
}
}
this.clusterer = new KMeansClusterer(measure);
}
}