Java SequenceFileDirValueIterable类代码示例

import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable; //导入依赖的package包/类
/***
 * PPCA: sequential PPCA based on the paper from Tipping and Bishop
 * 
 * @param conf
 *          the configuration
 * @param input
 *          the path to the input matrix Y
 * @param output
 *          the output path (not used currently)
 * @param nRows
 *          number or rows in Y
 * @param nCols
 *          number of columns in Y
 * @param nPCs
 *          number of desired principal components
 * @return the error
 * @throws Exception
 */
double runSequential(Configuration conf, Path input, Path output,
    final int nRows, final int nCols, final int nPCs) throws Exception {
  Matrix centralY = new DenseMatrix(nRows, nCols);
  FileSystem fs = FileSystem.get(input.toUri(), conf);
  if (fs.listStatus(input).length == 0) {
    System.err.println("No file under " + input);
    return 0;
  }
  int row = 0;
  for (VectorWritable vw : new SequenceFileDirValueIterable<VectorWritable>(
      input, PathType.LIST, null, conf)) {
    centralY.assignRow(row, vw.get());
    row++;
  }
  Matrix centralC = PCACommon.randomMatrix(nCols, nPCs);
  double ss = PCACommon.randSS();
  InitialValues initVal = new InitialValues(centralC, ss);
  // Matrix sampledYe = sample(centralY);
  // runSequential(conf, sampledYe, initVal, 100);
  double error = runSequential(conf, centralY, initVal, 100);
  return error;
}
 

import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable; //导入依赖的package包/类
/**
 * PPCA: sequential PPCA based on the matlab implementation of Jacob Verbeek
 * 
 * @param conf
 *          the configuration
 * @param input
 *          the path to the input matrix Y
 * @param output
 *          the output path (not used currently)
 * @param nRows
 *          number or rows in Y
 * @param nCols
 *          number of columns in Y
 * @param nPCs
 *          number of desired principal components
 * @return the error
 * @throws Exception
 */
double runSequential_JacobVersion(Configuration conf, Path input,
    Path output, final int nRows, final int nCols, final int nPCs) throws Exception {
  Matrix centralY = new DenseMatrix(nRows, nCols);
  FileSystem fs = FileSystem.get(input.toUri(), conf);
  if (fs.listStatus(input).length == 0) {
    System.err.println("No file under " + input);
    return 0;
  }
  int row = 0;
  for (VectorWritable vw : new SequenceFileDirValueIterable<VectorWritable>(
      input, PathType.LIST, null, conf)) {
    centralY.assignRow(row, vw.get());
    row++;
  }
  Matrix C = PCACommon.randomMatrix(nCols, nPCs);
  double ss = PCACommon.randSS();
  InitialValues initVal = new InitialValues(C, ss);
  double error = runSequential_JacobVersion(conf, centralY, initVal, 100);
  return error;
}
 

import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable; //导入依赖的package包/类
/**
 * Create a list of SoftClusters from whatever type is passed in as the prior
 * 
 * @param conf
 *          the Configuration
 * @param clusterPath
 *          the path to the prior Clusters
 * @param clusters
 *          a List<Cluster> to put values into
 */
public static void configureWithClusterInfo(Configuration conf, Path clusterPath, List<Cluster> clusters) {
  for (Writable value : new SequenceFileDirValueIterable<Writable>(clusterPath, PathType.LIST,
      PathFilters.partFilter(), conf)) {
    Class<? extends Writable> valueClass = value.getClass();
    
    if (valueClass.equals(ClusterWritable.class)) {
      ClusterWritable clusterWritable = (ClusterWritable) value;
      value = clusterWritable.getValue();
      valueClass = value.getClass();
    }
    
    if (valueClass.equals(Kluster.class)) {
      // get the cluster info
      Kluster cluster = (Kluster) value;
      clusters.add(new SoftCluster(cluster.getCenter(), cluster.getId(), cluster.getMeasure()));
    } else if (valueClass.equals(SoftCluster.class)) {
      // get the cluster info
      clusters.add((SoftCluster) value);
    } else if (valueClass.equals(Canopy.class)) {
      // get the cluster info
      Canopy canopy = (Canopy) value;
      clusters.add(new SoftCluster(canopy.getCenter(), canopy.getId(), canopy.getMeasure()));
    } else {
      throw new IllegalStateException("Bad value class: " + valueClass);
    }
  }
  
}
 

import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable; //导入依赖的package包/类
/**
 * Create a list of Klusters from whatever Cluster type is passed in as the prior
 * 
 * @param conf
 *          the Configuration
 * @param clusterPath
 *          the path to the prior Clusters
 * @param clusters
 *          a List<Cluster> to put values into
 */
public static void configureWithClusterInfo(Configuration conf, Path clusterPath, Collection<Cluster> clusters) {
  for (Writable value : new SequenceFileDirValueIterable<Writable>(clusterPath, PathType.LIST,
      PathFilters.partFilter(), conf)) {
    Class<? extends Writable> valueClass = value.getClass();
    if (valueClass.equals(ClusterWritable.class)) {
      ClusterWritable clusterWritable = (ClusterWritable) value;
      value = clusterWritable.getValue();
      valueClass = value.getClass();
    }
    log.debug("Read 1 Cluster from {}", clusterPath);
    
    if (valueClass.equals(Kluster.class)) {
      // get the cluster info
      clusters.add((Kluster) value);
    } else if (valueClass.equals(Canopy.class)) {
      // get the cluster info
      Canopy canopy = (Canopy) value;
      clusters.add(new Kluster(canopy.getCenter(), canopy.getId(), canopy.getMeasure()));
    } else {
      throw new IllegalStateException("Bad value class: " + valueClass);
    }
  }
}
 

import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable; //导入依赖的package包/类
public static List<List<Cluster>> readClusters(Configuration conf, Path output)
		throws IOException {
	List<List<Cluster>> Clusters = Lists.newArrayList();
	FileSystem fs = FileSystem.get(output.toUri(), conf);

	for (FileStatus s : fs.listStatus(output, new ClustersFilter())) {
		List<Cluster> clusters = Lists.newArrayList();
		for (ClusterWritable value : new SequenceFileDirValueIterable<ClusterWritable>(
				s.getPath(), PathType.LIST, PathFilters.logsCRCFilter(),
				conf)) {
			Cluster cluster = value.getValue();
			clusters.add(cluster);
		}
		Clusters.add(clusters);
	}
	return Clusters;
}
 

import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable; //导入依赖的package包/类
@Test
public void crossTestIterationOfMapReducePPCASequentialPPCA() throws Exception {
  Matrix C_central = PCACommon.randomMatrix(D, d);
  double ss = PCACommon.randSS();
  InitialValues initValSeq = new InitialValues(C_central, ss);
  InitialValues initValMR = new InitialValues(C_central.clone(), ss);

  //1. run sequential
  Matrix Ye_central = new DenseMatrix(N, D);
  int row = 0;
  for (VectorWritable vw : new SequenceFileDirValueIterable<VectorWritable>(
      input, PathType.LIST, null, conf)) {
    Ye_central.assignRow(row, vw.get());
    row++;
  }
  double bishopSeqErr = ppcaDriver.runSequential(conf, Ye_central, initValSeq, 1);
  
  //2. run mapreduce
  DistributedRowMatrix Ye = new DistributedRowMatrix(input, tmp, N, D);
  Ye.setConf(conf);
  double bishopMRErr = ppcaDriver.runMapReduce(conf, Ye, initValMR, output, N, D, d, 1, 1, 1, 1);
  
  Assert.assertEquals(
      "ss value is different in sequential and mapreduce PCA", initValSeq.ss,
      initValMR.ss, EPSILON);
  double seqCTrace = PCACommon.trace(initValSeq.C);
  double mrCTrace = PCACommon.trace(initValMR.C);
  Assert.assertEquals(
      "C value is different in sequential and mapreduce PCA", seqCTrace,
      mrCTrace, EPSILON);
  Assert.assertEquals(
      "The PPCA error between sequntial and mapreduce methods is too different: "
          + bishopSeqErr + "!= " + bishopMRErr, bishopSeqErr, bishopMRErr, EPSILON);
}
 

import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable; //导入依赖的package包/类
/**
 * Build a directory of Canopy clusters from the input vectors and other
 * arguments. Run sequential execution
 * 
 * @param input
 *          the Path to the directory containing input vectors
 * @param output
 *          the Path for all output directories
 * @param measure
 *          the DistanceMeasure
 * @param t1
 *          the double T1 distance metric
 * @param t2
 *          the double T2 distance metric
 * @param clusterFilter
 *          the int minimum size of canopies produced
 * @return the canopy output directory Path
 */
private static Path buildClustersSeq(Path input, Path output,
    DistanceMeasure measure, double t1, double t2, int clusterFilter)
    throws IOException {
  CanopyClusterer clusterer = new CanopyClusterer(measure, t1, t2);
  Collection<Canopy> canopies = Lists.newArrayList();
  Configuration conf = new Configuration();
  FileSystem fs = FileSystem.get(input.toUri(), conf);

  for (VectorWritable vw : new SequenceFileDirValueIterable<VectorWritable>(
      input, PathType.LIST, PathFilters.logsCRCFilter(), conf)) {
    clusterer.addPointToCanopies(vw.get(), canopies);
  }

  Path canopyOutputDir = new Path(output, Cluster.CLUSTERS_DIR + '0'+ Cluster.FINAL_ITERATION_SUFFIX);
  Path path = new Path(canopyOutputDir, "part-r-00000");
  SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf, path,
      Text.class, ClusterWritable.class);
  ClusterWritable clusterWritable = new ClusterWritable();
  try {
    for (Canopy canopy : canopies) {
      canopy.computeParameters();
      if (log.isDebugEnabled()) {
        log.debug("Writing Canopy:{} center:{} numPoints:{} radius:{}",
            new Object[] { canopy.getIdentifier(),
                AbstractCluster.formatVector(canopy.getCenter(), null),
                canopy.getNumObservations(),
                AbstractCluster.formatVector(canopy.getRadius(), null) });
      }
      if (canopy.getNumObservations() > clusterFilter) {
      	clusterWritable.setValue(canopy);
      	writer.append(new Text(canopy.getIdentifier()), clusterWritable);
      }
    }
  } finally {
    Closeables.closeQuietly(writer);
  }
  return canopyOutputDir;
}
 

import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable; //导入依赖的package包/类
public void readFromSeqFiles(Configuration conf, Path path) throws IOException {
  Configuration config = new Configuration();
  List<Cluster> clusters = Lists.newArrayList();
  for (ClusterWritable cw : new SequenceFileDirValueIterable<ClusterWritable>(path, PathType.LIST,
      PathFilters.logsCRCFilter(), config)) {
    Cluster cluster = cw.getValue();
    cluster.configure(conf);
    clusters.add(cluster);
  }
  this.models = clusters;
  modelClass = models.get(0).getClass().getName();
  this.policy = readPolicy(path);
}
 

import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable; //导入依赖的package包/类
public static List<MeanShiftCanopy> getCanopies(Configuration conf) {
  String statePath = conf.get(MeanShiftCanopyDriver.STATE_IN_KEY);
  List<MeanShiftCanopy> canopies = Lists.newArrayList();
  Path path = new Path(statePath);
  for (ClusterWritable clusterWritable 
       : new SequenceFileDirValueIterable<ClusterWritable>(path, PathType.LIST, PathFilters.logsCRCFilter(), conf)) {
    MeanShiftCanopy canopy = (MeanShiftCanopy)clusterWritable.getValue();
    canopies.add(canopy);
  }
  return canopies;
}
 

import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable; //导入依赖的package包/类
/**
 * Iterate over data using a prior-trained ClusterClassifier, for a number of iterations using a sequential
 * implementation
 * 
 * @param conf
 *          the Configuration
 * @param inPath
 *          a Path to input VectorWritables
 * @param priorPath
 *          a Path to the prior classifier
 * @param outPath
 *          a Path of output directory
 * @param numIterations
 *          the int number of iterations to perform
 */
public static void iterateSeq(Configuration conf, Path inPath, Path priorPath, Path outPath, int numIterations)
  throws IOException {
  ClusterClassifier classifier = new ClusterClassifier();
  classifier.readFromSeqFiles(conf, priorPath);
  Path clustersOut = null;
  int iteration = 1;
  while (iteration <= numIterations) {
    for (VectorWritable vw : new SequenceFileDirValueIterable<VectorWritable>(inPath, PathType.LIST,
        PathFilters.logsCRCFilter(), conf)) {
      Vector vector = vw.get();
      // classification yields probabilities
      Vector probabilities = classifier.classify(vector);
      // policy selects weights for models given those probabilities
      Vector weights = classifier.getPolicy().select(probabilities);
      // training causes all models to observe data
      for (Iterator<Vector.Element> it = weights.iterateNonZero(); it.hasNext();) {
        int index = it.next().index();
        classifier.train(index, vector, weights.get(index));
      }
    }
    // compute the posterior models
    classifier.close();
    // update the policy
    classifier.getPolicy().update(classifier);
    // output the classifier
    clustersOut = new Path(outPath, Cluster.CLUSTERS_DIR + iteration);
    classifier.writeToSeqFiles(clustersOut);
    FileSystem fs = FileSystem.get(outPath.toUri(), conf);
    iteration++;
    if (isConverged(clustersOut, conf, fs)) {
      break;
    }
  }
  Path finalClustersIn = new Path(outPath, Cluster.CLUSTERS_DIR + (iteration - 1) + Cluster.FINAL_ITERATION_SUFFIX);
  FileSystem.get(clustersOut.toUri(), conf).rename(clustersOut, finalClustersIn);
}
 

import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable; //导入依赖的package包/类
/**
 * Classifies the vector into its respective cluster.
 * 
 * @param input
 *          the path containing the input vector.
 * @param clusterModels
 *          the clusters
 * @param clusterClassifier
 *          used to classify the vectors into different clusters
 * @param output
 *          the path to store classified data
 * @param clusterClassificationThreshold
 * @param emitMostLikely
 *          TODO
 * @throws IOException
 */
private static void selectCluster(Path input, List<Cluster> clusterModels, ClusterClassifier clusterClassifier,
    Path output, Double clusterClassificationThreshold, boolean emitMostLikely) throws IOException {
  Configuration conf = new Configuration();
  SequenceFile.Writer writer = new SequenceFile.Writer(input.getFileSystem(conf), conf, new Path(output,
      "part-m-" + 0), IntWritable.class, WeightedVectorWritable.class);
  for (VectorWritable vw : new SequenceFileDirValueIterable<VectorWritable>(input, PathType.LIST,
      PathFilters.logsCRCFilter(), conf)) {
    Vector pdfPerCluster = clusterClassifier.classify(vw.get());
    if (shouldClassify(pdfPerCluster, clusterClassificationThreshold)) {
      classifyAndWrite(clusterModels, clusterClassificationThreshold, emitMostLikely, writer, vw, pdfPerCluster);
    }
  }
  writer.close();
}