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/*
* 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.streaming.mapreduce;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.mahout.clustering.ClusteringUtils;
import org.apache.mahout.clustering.streaming.cluster.StreamingKMeans;
import org.apache.mahout.math.Centroid;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.math.neighborhood.UpdatableSearcher;
public class StreamingKMeansMapper extends Mapper {
private static final int NUM_ESTIMATE_POINTS = 1000;
/**
* The clusterer object used to cluster the points received by this mapper online.
*/
private StreamingKMeans clusterer;
/**
* Number of points clustered so far.
*/
private int numPoints = 0;
private boolean estimateDistanceCutoff = false;
private List estimatePoints;
@Override
public void setup(Context context) {
// At this point the configuration received from the Driver is assumed to be valid.
// No other checks are made.
Configuration conf = context.getConfiguration();
UpdatableSearcher searcher = StreamingKMeansUtilsMR.searcherFromConfiguration(conf);
int numClusters = conf.getInt(StreamingKMeansDriver.ESTIMATED_NUM_MAP_CLUSTERS, 1);
double estimatedDistanceCutoff = conf.getFloat(StreamingKMeansDriver.ESTIMATED_DISTANCE_CUTOFF,
StreamingKMeansDriver.INVALID_DISTANCE_CUTOFF);
if (estimatedDistanceCutoff == StreamingKMeansDriver.INVALID_DISTANCE_CUTOFF) {
estimateDistanceCutoff = true;
estimatePoints = new ArrayList<>();
}
// There is no way of estimating the distance cutoff unless we have some data.
clusterer = new StreamingKMeans(searcher, numClusters, estimatedDistanceCutoff);
}
private void clusterEstimatePoints() {
clusterer.setDistanceCutoff(ClusteringUtils.estimateDistanceCutoff(
estimatePoints, clusterer.getDistanceMeasure()));
clusterer.cluster(estimatePoints);
estimateDistanceCutoff = false;
}
@Override
public void map(Writable key, VectorWritable point, Context context) {
Centroid centroid = new Centroid(numPoints++, point.get(), 1);
if (estimateDistanceCutoff) {
if (numPoints < NUM_ESTIMATE_POINTS) {
estimatePoints.add(centroid);
} else if (numPoints == NUM_ESTIMATE_POINTS) {
clusterEstimatePoints();
}
} else {
clusterer.cluster(centroid);
}
}
@Override
public void cleanup(Context context) throws IOException, InterruptedException {
// We should cluster the points at the end if they haven't yet been clustered.
if (estimateDistanceCutoff) {
clusterEstimatePoints();
}
// Reindex the centroids before passing them to the reducer.
clusterer.reindexCentroids();
// All outputs have the same key to go to the same final reducer.
for (Centroid centroid : clusterer) {
context.write(new IntWritable(0), new CentroidWritable(centroid));
}
}
}
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