org.apache.mahout.clustering.streaming.mapreduce.StreamingKMeansMapper.java Source code

Introduction

Here is the source code for org.apache.mahout.clustering.streaming.mapreduce.StreamingKMeansMapper.java

Source

/*
 * 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.List;

import com.google.common.collect.Lists;
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<Writable, VectorWritable, IntWritable, CentroidWritable> {
    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<Centroid> 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 = Lists.newArrayList();
        }
        // 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));
        }
    }
}