org.apache.mahout.clustering.spectral.VectorMatrixMultiplicationJob Maven / Gradle / Ivy

/**
 * 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.spectral;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.math.function.Functions;
import org.apache.mahout.math.hadoop.DistributedRowMatrix;

/**
 * This class handles the three-way multiplication of the digonal matrix
 * and the Markov transition matrix inherent in the Eigencuts algorithm.
 * The equation takes the form:
 *
 * {@code W = D^(1/2) * M * D^(1/2)}
 *
 * Since the diagonal matrix D has only n non-zero elements, it is represented
 * as a dense vector in this job, rather than a full n-by-n matrix. This job
 * performs the multiplications and returns the new DRM.
 */
public final class VectorMatrixMultiplicationJob {

  private VectorMatrixMultiplicationJob() {
  }

  /**
   * Invokes the job.
   * @param markovPath Path to the markov DRM's sequence files
   */
  public static DistributedRowMatrix runJob(Path markovPath, Vector diag, Path outputPath)
    throws IOException, ClassNotFoundException, InterruptedException {
    
    return runJob(markovPath, diag, outputPath, new Path(outputPath, "tmp"));
  }

  public static DistributedRowMatrix runJob(Path markovPath, Vector diag, Path outputPath, Path tmpPath)
    throws IOException, ClassNotFoundException, InterruptedException {

    // set up the serialization of the diagonal vector
    Configuration conf = new Configuration();
    FileSystem fs = FileSystem.get(markovPath.toUri(), conf);
    markovPath = fs.makeQualified(markovPath);
    outputPath = fs.makeQualified(outputPath);
    Path vectorOutputPath = new Path(outputPath.getParent(), "vector");
    VectorCache.save(new IntWritable(Keys.DIAGONAL_CACHE_INDEX), diag, vectorOutputPath, conf);

    // set up the job itself
    Job job = new Job(conf, "VectorMatrixMultiplication");
    job.setInputFormatClass(SequenceFileInputFormat.class);
    job.setOutputKeyClass(IntWritable.class);
    job.setOutputValueClass(VectorWritable.class);
    job.setOutputFormatClass(SequenceFileOutputFormat.class);
    job.setMapperClass(VectorMatrixMultiplicationMapper.class);
    job.setNumReduceTasks(0);

    FileInputFormat.addInputPath(job, markovPath);
    FileOutputFormat.setOutputPath(job, outputPath);

    job.setJarByClass(VectorMatrixMultiplicationJob.class);

    boolean succeeded = job.waitForCompletion(true);
    if (!succeeded) {
      throw new IllegalStateException("Job failed!");
    }

    // build the resulting DRM from the results
    return new DistributedRowMatrix(outputPath, tmpPath,
        diag.size(), diag.size());
  }
  
  public static class VectorMatrixMultiplicationMapper
    extends Mapper {
    
    private Vector diagonal;
    
    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
      // read in the diagonal vector from the distributed cache
      super.setup(context);
      Configuration config = context.getConfiguration();
      diagonal = VectorCache.load(config);
      if (diagonal == null) {
        throw new IOException("No vector loaded from cache!");
      }
      if (!(diagonal instanceof DenseVector)) {
        diagonal = new DenseVector(diagonal);
      }
    }
    
    @Override
    protected void map(IntWritable key, VectorWritable row, Context ctx) 
      throws IOException, InterruptedException {
      
      for (Vector.Element e : row.get().all()) {
        double dii = Functions.SQRT.apply(diagonal.get(key.get()));
        double djj = Functions.SQRT.apply(diagonal.get(e.index()));
        double mij = e.get();
        e.set(dii * mij * djj);
      }
      ctx.write(key, row);
    }
    
    /**
     * Performs the setup of the Mapper. Used by unit tests.
     * @param diag
     */
    void setup(Vector diag) {
      this.diagonal = diag;
    }
  }
}