我试图在mapreduce上执行weka，而标准输出总是空的
这是运行整个程序的类。它负责获取用户的输入、设置Map器和还原器、组织weka输入等。

public class WekDoop {

     * The main method of this program. 
     * Precondition: arff file is uploaded into HDFS and the correct
     * number of parameters were passed into the JAR file when it was run
     * 
     * @param args
     * @throws Exception
     */
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();

        // Make sure we have the correct number of arguments passed into the program
        if (args.length != 4) {
          System.err.println("Usage: WekDoop <# of splits> <classifier> <input file> <output file>");
          System.exit(1);
        }

        // configure the job using the command line args
        conf.setInt("Run-num.splits", Integer.parseInt(args[0]));
        conf.setStrings("Run.classify", args[1]);
        conf.set("io.serializations", "org.apache.hadoop.io.serializer.JavaSerialization," + "org.apache.hadoop.io.serializer.WritableSerialization");

        // Configure the jobs main class, mapper and reducer
        // TODO: Make the Job name print the name of the currently running classifier
        Job job = new Job(conf, "WekDoop");
        job.setJarByClass(WekDoop.class);
        job.setMapperClass(WekaMap.class);
        job.setReducerClass(WekaReducer.class);

        // Start with 1
        job.setNumReduceTasks(1);

        // This section sets the values of the <K2, V2>
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(weka.classifiers.bayes.NaiveBayes.class);
        job.setOutputValueClass(AggregateableEvaluation.class);

        // Set the input and output directories based on command line args
        FileInputFormat.addInputPath(job, new Path(args[2]));
        FileOutputFormat.setOutputPath(job, new Path(args[3]));

        // Set the input type of the environment
        // (In this case we are overriding TextInputFormat)
        job.setInputFormatClass(WekaInputFormat.class);

        // wait until the job is complete to exit
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

Map器类
这个类是weka分类器的Map器，它被赋予一个数据块，并设置一个在该数据上运行的分类器。在这个方法中还有很多其他的处理。

public  class WekaMap extends Mapper<Object, Text, Text, AggregateableEvaluation> {
    private Instances randData = null;
    private Classifier cls = null;

    private AggregateableEvaluation eval = null;
    private Classifier clsCopy = null;

    // Run 10 mappers
    private String numMaps = "10";

    // TODO: Make sure this is not hard-coded -- preferably a command line arg
    // Set the classifier
    private String classname = "weka.classifiers.bayes.NaiveBayes";
    private int seed = 20;

    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
        String line = value.toString();
        System.out.println("CURRENT LINE: " + line);

        //line = "/home/ubuntu/Workspace/hadoop-1.1.0/hadoop-data/spambase_processed.arff";

        Configuration conf = new Configuration();
        FileSystem fileSystem = FileSystem.get(conf);

        Path path = new Path("/home/hduser/very_small_spam.arff");

        // Make sure the file exists...
        if (!fileSystem.exists(path)) {
            System.out.println("File does not exists");
            return;
        }

        JobID test = context.getJobID();
        TaskAttemptID tid = context.getTaskAttemptID();

        // Set up the weka configuration
        Configuration wekaConfig = context.getConfiguration();
        numMaps = wekaConfig.get("Run-num.splits");
        classname = wekaConfig.get("Run.classify");

        String[] splitter = tid.toString().split("_");
        String jobNumber = "";
        int n = 0;

        if (splitter[4].length() > 0) {
            jobNumber = splitter[4].substring(splitter[4].length() - 1);
            n = Integer.parseInt(jobNumber);
        }

        FileSystem fs = FileSystem.get(context.getConfiguration());

        System.out.println("PATH: " + path);

        // Read in the data set
        context.setStatus("Reading in the arff file...");
        readArff(fs, path.toString());
        context.setStatus("Done reading arff! Initializing aggregateable eval...");

        try {
            eval = new AggregateableEvaluation(randData);
        }
        catch (Exception e1) {
            e1.printStackTrace();
        }

        // Split the data into two sets: Training set and a testing set
        // this will allow us to use a little bit of data to train the classifier
        // before running the classifier on the rest of the dataset
        Instances trainInstance = randData.trainCV(Integer.parseInt(numMaps), n);
        Instances testInstance = randData.testCV(Integer.parseInt(numMaps), n);

        // Set parameters to be passed to the classifiers
        String[] opts = new String[3];
        if (classname.equals("weka.classifiers.lazy.IBk")) {
            opts[0] = "";
            opts[1] = "-K";
            opts[2] = "1";
        }
        else if (classname.equals("weka.classifiers.trees.J48")) {
            opts[0] = "";
            opts[1] = "-C";
            opts[2] = "0.25";
        }
        else if (classname.equals("weka.classifiers.bayes.NaiveBayes")) {
            opts[0] = "";
            opts[1] = "";
            opts[2] = "";
        }
        else {
            opts[0] = "";
            opts[1] = "";
            opts[2] = "";
        }

        // Start setting up the classifier and its various options
        try {
          cls = (Classifier) Utils.forName(Classifier.class, classname, opts);
        }
        catch (Exception e) {
            e.printStackTrace();
        }

        // These are all used for timing different processes
        long beforeAbstract = 0;
        long beforeBuildClass = 0;
        long afterBuildClass = 0;
        long beforeEvalClass = 0;
        long afterEvalClass = 0;

        try {
            // Create the classifier and record how long it takes to set up 
            context.setStatus("Creating the classifier...");
            System.out.println(new Timestamp(System.currentTimeMillis()));
            beforeAbstract = System.currentTimeMillis();
            clsCopy = AbstractClassifier.makeCopy(cls);
            beforeBuildClass = System.currentTimeMillis();
            System.out.println(new Timestamp(System.currentTimeMillis()));

            // Train the classifier on the training set and record how long this takes
            context.setStatus("Training the classifier...");
            clsCopy.buildClassifier(trainInstance);
            afterBuildClass = System.currentTimeMillis();
            System.out.println(new Timestamp(System.currentTimeMillis()));
            beforeEvalClass = System.currentTimeMillis();

            // Run the classifer on the rest of the data set and record its duration as well
            context.setStatus("Evaluating the model...");
            eval.evaluateModel(clsCopy, testInstance);
            afterEvalClass = System.currentTimeMillis();
            System.out.println(new Timestamp(System.currentTimeMillis()));

            // We are done this iteration!
            context.setStatus("Complete");
        }
        catch (Exception e) {
            System.out.println("Debugging strarts here!");
            e.printStackTrace();
        }

        // calculate the total times for each section
        long abstractTime = beforeBuildClass - beforeAbstract;
        long buildTime = afterBuildClass - beforeBuildClass;
        long evalTime = afterEvalClass - beforeEvalClass;

        // Print out the times
        System.out.println("The value of creation time: " + abstractTime);
        System.out.println("The value of Build time: " + buildTime);
        System.out.println("The value of Eval time: " + evalTime);

        context.write(new Text(line), eval);
      }

    /**
     * This can be used to write out the results on HDFS, but it is not essential
     * to the success of this project. If time allows, we can implement it.
     */
      public void writeResult() {    

      }

      /**
       * This method reads in the arff file that is provided to the program.
       * Nothing really special about the way the data is handled.
       * 
       * @param fs
       * @param filePath
       * @throws IOException
       * @throws InterruptedException
       */
      public void readArff(FileSystem fs, String filePath) throws IOException, InterruptedException {
          BufferedReader reader;
          DataInputStream d;
          ArffReader arff;
          Instance inst;
          Instances data;

          try {
              // Read in the data using a ton of wrappers
              d = new DataInputStream(fs.open(new Path(filePath)));
              reader = new BufferedReader(new InputStreamReader(d));
              arff = new ArffReader(reader, 100000);
              data = arff.getStructure();
              data.setClassIndex(data.numAttributes() - 1);

              // Add each line to the input stream
              while ((inst = arff.readInstance(data)) != null) {
                  data.add(inst);
              }

              reader.close();

              Random rand = new Random(seed);
              randData = new Instances(data);
              randData.randomize(rand);

              // This is how weka handles the sampling of the data
              // the stratify method splits up the data to cross validate it
              if (randData.classAttribute().isNominal()) {
                  randData.stratify(Integer.parseInt(numMaps));
              }
          }
          catch (IOException e) {
              e.printStackTrace();
          }
    }
}

减速器等级
这个类是weka分类器输出的一个缩减器，它从Map器中得到一堆交叉验证的数据块，它的工作是将数据聚合到一个解决方案中。

public  class WekaReducer extends Reducer<Text, AggregateableEvaluation, Text, IntWritable> {
     Text result = new Text();
     Evaluation evalAll = null;
     IntWritable test = new IntWritable();

     AggregateableEvaluation aggEval;

    /**
     * The reducer method takes all the stratified, cross-validated
     * values from the mappers in a list and uses an aggregatable evaluation to consolidate
     * them.
     */
    public void reduce(Text key, Iterable<AggregateableEvaluation> values, Context context) throws IOException, InterruptedException {      
        int sum = 0;

        // record how long it takes to run the aggregation
        System.out.println(new Timestamp(System.currentTimeMillis()));
        long beforeReduceTime = System.currentTimeMillis();

        // loop through each of the values and "aggregate"
        // which basically means to consolidate the values
        for (AggregateableEvaluation val : values) {
            System.out.println("IN THE REDUCER!");

            // The first time through, give aggEval a value
            if (sum == 0) {
                try {
                    aggEval = val;
                }
                catch (Exception e) {
                    e.printStackTrace();
                }
            }
            else {
                // combine the values
                aggEval.aggregate(val);
            }

            try {
                // This is what is taken from the mapper to be aggregated
                System.out.println("This is the map result");
                System.out.println(aggEval.toMatrixString());
            }
            catch (Exception e) {
                e.printStackTrace();
            }                       

            sum += 1;
        }

        // Here is where the typical weka matrix output is generated
        try {
            System.out.println("This is reduce matrix");
            System.out.println(aggEval.toMatrixString());
        }
        catch (Exception e) {
            e.printStackTrace();
        }

        // calculate the duration of the aggregation
        context.write(key, new IntWritable(sum));
        long afterReduceTime = System.currentTimeMillis();
        long reduceTime = afterReduceTime - beforeReduceTime;

        // display the output
        System.out.println("The value of reduce time is: " + reduceTime);
        System.out.println(new Timestamp(System.currentTimeMillis()));
    }
}

最后是inputformatclass
获取一个jobcontext并返回一个数据列表，这些数据被拆分为多个部分基本上这是一种处理大型数据集的方法。这种方法允许我们将一个大数据集分割成更小的数据块，以便在工作节点之间传递（或者在我们的例子中，只是为了让工作变得更轻松，并将数据块传递给单个节点，这样它就不会被一个大数据集淹没）

public class WekaInputFormat extends TextInputFormat {

    public List<InputSplit> getSplits(JobContext job) throws IOException {
        long minSize = Math.max(getFormatMinSplitSize(), getMinSplitSize(job));
        long maxSize = getMaxSplitSize(job);

        List<InputSplit> splits = new ArrayList<InputSplit>();
        for (FileStatus file: listStatus(job)) {
            Path path = file.getPath();
            FileSystem fs = path.getFileSystem(job.getConfiguration());

            //number of bytes in this file
            long length = file.getLen();
            BlockLocation[] blkLocations = fs.getFileBlockLocations(file, 0, length);

            // make sure this is actually a valid file
            if(length != 0) {
                // set the number of splits to make. NOTE: the value can be changed to anything
                int count = job.getConfiguration().getInt("Run-num.splits", 1);
                for(int t = 0; t < count; t++) {
                    //split the file and add each chunk to the list
                    splits.add(new FileSplit(path, 0, length, blkLocations[0].getHosts())); 
                }
            }
            else {
                // Create empty array for zero length files
                splits.add(new FileSplit(path, 0, length, new String[0]));
            }
        }
        return splits;
    }
}