org.apache.mahout.clustering.streaming.mapreduce.StreamingKMeansMapper Maven / Gradle / Ivy

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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));
    }
  }
}