Twenty Newsgroups Classification任务之二seq2sparse（2）

接上篇，SequenceFileTokenizerMapper的输出文件在/home/mahout/mahout-work-mahout0/20news-vectors/tokenized-documents/part-m-00000文件即可查看，同时可以编写下面的代码来读取该文件（该代码是根据前面读出聚类中心点文件改编的），如下：

package mahout.fansy.test.bayes.read;

import java.util.ArrayList;
import java.util.List;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Writable;
import org.apache.mahout.common.StringTuple;
import org.apache.mahout.common.iterator.sequencefile.PathFilters;
import org.apache.mahout.common.iterator.sequencefile.PathType;
import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable;

public class ReadFromTokenizedDocuments {

	/**
	 * @param args
	 */
	private static Configuration conf;

	public static void main(String[] args) {
		conf=new Configuration();
		conf.set("mapred.job.tracker", "ubuntu:9001");
		String path="hdfs://ubuntu:9000/home/mahout/mahout-work-mahout0/20news-vectors/tokenized-documents/part-m-00000";

		getValue(path,conf);
	}

	 /**
     * 把序列文件读入到一个变量中；
     * @param path 序列文件
     * @param conf  Configuration
     * @return  序列文件读取的变量
     */
    public static List<StringTuple> getValue(String path,Configuration conf){
    	Path hdfsPath=new Path(path);
    	List<StringTuple> list = new ArrayList<StringTuple>();
    	for (Writable value : new SequenceFileDirValueIterable<Writable>(hdfsPath, PathType.LIST,
    	        PathFilters.partFilter(), conf)) {
    	      Class<? extends Writable> valueClass = value.getClass();
    	      if (valueClass.equals(StringTuple.class)) {
    	    	  StringTuple st = (StringTuple) value;
    	          list.add(st);
    	      } else {
    	        throw new IllegalStateException("Bad value class: " + valueClass);
    	      }
    	    }
    	return list;
    }

}

通过上面的文件可以读取到第一个StringTuple的单词个数有1320个（去掉stop words的单词数）；

然后就又是一堆参数的设置，一直到267行，判断processIdf是否为非true，因为前面设置的是tfdif，所以这里进入else代码块，如下：

if (!processIdf) {
        DictionaryVectorizer.createTermFrequencyVectors(tokenizedPath, outputDir, tfDirName, conf, minSupport, maxNGramSize,
          minLLRValue, norm, logNormalize, reduceTasks, chunkSize, sequentialAccessOutput, namedVectors);
      } else {
        DictionaryVectorizer.createTermFrequencyVectors(tokenizedPath, outputDir, tfDirName, conf, minSupport, maxNGramSize,
          minLLRValue, -1.0f, false, reduceTasks, chunkSize, sequentialAccessOutput, namedVectors);
      }

这里直接调用DictionaryVectorizer的createTermFrequencyVectors方法，进入该方法（DictionaryVectorizer的145行），可以看到首先也是一些参数的设置，然后就到了startWordCounting方法了，进入这个方法可以看到这个是一个Job的基本设置，其Mapper、Combiner、Reducer分别为：TermCountMapper、TermCountCombiner、TermCountReducer，下面分别来看各个部分的作用（其实和最基本的wordcount很相似）：

TermCountMapper，首先贴代码：

protected void map(Text key, StringTuple value, final Context context) throws IOException, InterruptedException {
    OpenObjectLongHashMap<String> wordCount = new OpenObjectLongHashMap<String>();
    for (String word : value.getEntries()) {
      if (wordCount.containsKey(word)) {
        wordCount.put(word, wordCount.get(word) + 1);
      } else {
        wordCount.put(word, 1);
      }
    }
    wordCount.forEachPair(new ObjectLongProcedure<String>() {
      @Override
      public boolean apply(String first, long second) {
        try {
          context.write(new Text(first), new LongWritable(second));
        } catch (IOException e) {
          context.getCounter("Exception", "Output IO Exception").increment(1);
        } catch (InterruptedException e) {
          context.getCounter("Exception", "Interrupted Exception").increment(1);
        }
        return true;
      }
    });

该部分代码首先定义了一个Mahout开发人员定义的Map类，然后遍历value中的各个单词（比如第一个value中有1320个单词）；当遇到map中没有的单词就把其加入map中，否则把map中该单词的数量加1更新原来的单词的数量，即for循环里面做的事情；然后就是forEachPair方法了，这里应该是复写了该方法？好像是直接新建了一个类然后把这个新建的类作为forEachPair的参数；直接看context.write吧，应该是把wordCount(这个变量含有每个单词和它的计数)中的各个单词和单词计数分别作为key和value输出；

然后是TermCountCombiner和TermCountReducer，这两个代码一样的和当初学习Hadoop入门的第一个例子是一样的，这里就不多说了。查看log信息，可以看到reduce一共输出93563个单词。

然后就到了createDictionaryChunks函数了，进入到DictionaryVectorizer的215行中的该方法：

 List<Path> chunkPaths = Lists.newArrayList();

    Configuration conf = new Configuration(baseConf);

    FileSystem fs = FileSystem.get(wordCountPath.toUri(), conf);

    long chunkSizeLimit = chunkSizeInMegabytes * 1024L * 1024L;
    int chunkIndex = 0;
    Path chunkPath = new Path(dictionaryPathBase, DICTIONARY_FILE + chunkIndex);
    chunkPaths.add(chunkPath);

    SequenceFile.Writer dictWriter = new SequenceFile.Writer(fs, conf, chunkPath, Text.class, IntWritable.class);

    try {
      long currentChunkSize = 0;
      Path filesPattern = new Path(wordCountPath, OUTPUT_FILES_PATTERN);
      int i = 0;
      for (Pair<Writable,Writable> record
           : new SequenceFileDirIterable<Writable,Writable>(filesPattern, PathType.GLOB, null, null, true, conf)) {
        if (currentChunkSize > chunkSizeLimit) {
          Closeables.closeQuietly(dictWriter);
          chunkIndex++;

          chunkPath = new Path(dictionaryPathBase, DICTIONARY_FILE + chunkIndex);
          chunkPaths.add(chunkPath);

          dictWriter = new SequenceFile.Writer(fs, conf, chunkPath, Text.class, IntWritable.class);
          currentChunkSize = 0;
        }

        Writable key = record.getFirst();
        int fieldSize = DICTIONARY_BYTE_OVERHEAD + key.toString().length() * 2 + Integer.SIZE / 8;
        currentChunkSize += fieldSize;
        dictWriter.append(key, new IntWritable(i++));
      }
      maxTermDimension[0] = i;
    } finally {
      Closeables.closeQuietly(dictWriter);
    }

这里看到新建了一个Writer，然后遍历该文件的key和value，但是只读取key值，即单词，然后把这些单词进行编码，即第一个单词用0和它对应，第二个单词用1和它对应。

上面代码使用的dictWriter查看变量并没有看到哪个属性是存储单词和对应id的，所以这里的写入文件的机制是append就写入？还是我没有找到正确的属性？待查。。。

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