Slaytanic

Word Co-occurrence一直不知道该怎么正确翻译, 单词相似度?还是共生单词?还是单词的共生矩阵?

这在统计里面是很常用的文本处理算法,用来度量一组文档集中所有出现频率最接近的词组.嗯,其实是上下文词组,不是单词.算是一个比较常用的算法,可以衍生出其他的统计算法.能用来做推荐,因为它能够提供的结果是”人们看了这个,也会看那个”.比如做一些协同过滤之外的购物商品的推荐,信用卡的风险分析,或者是计算大家都喜欢什么东西.

比如 I love you , 出现 “I love” 的同时往往伴随着 “love you” 的出现,不过中文的处理跟英文不一样,需要先用分词库做预处理.

按照Mapper, Reducer和Driver的方式拆分代码

Mapper程序:

package wco;
 
import java.io.IOException;
 
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
 
public class WCoMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
 
  @Override
  public void map(LongWritable key, Text value, Context context)
      throws IOException, InterruptedException {
     
    /*
     * 将行内容全部转换为小写格式.
     */
    String line_lc = value.toString().toLowerCase();
    String before = null;
     
    /*
     *  将行拆分成单词
     *  并且key是前一个单词加上后一个单词
     *  value 是 1
     */
    for (String word : line_lc.split("\\W+")) { //循环行内容,按照空格进行分割单词
      if (word.length() > 0) {
        if (before != null) { //如果前词不为空,则写入上下文(第一次前词一定是空,直接跳到下面的before = word)
          context.write(new Text(before + "," + word), new IntWritable(1));
        }
        before = word; //将现词赋值给前词
      }
    }
  }
}

 

Reducer程序:

package wco;
 
import java.io.IOException;
 
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
 
public class WCoReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
 
  @Override
  public void reduce(Text key, Iterable<IntWritable> values, Context context)
      throws IOException, InterruptedException {
 
    int wordCount = 0;
    for (IntWritable value : values) {
      wordCount += value.get(); //单纯计算word count
    }
    context.write(key, new IntWritable(wordCount));
  }
}

 

Driver程序就不解释了，天下的Driver都一样:

package wco;
 
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.Job;
 
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
 
public class WCo extends Configured implements Tool {
 
  @Override
  public int run(String[] args) throws Exception {
 
    if (args.length != 2) {
      System.out.printf("Usage: hadoop jar wco.WCo <input> <output>\n");
      return -1;
    }
 
    Job job = new Job(getConf());
    job.setJarByClass(WCo.class);
    job.setJobName("Word Co Occurrence");
 
    FileInputFormat.setInputPaths(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
 
    job.setMapperClass(WCoMapper.class);
    job.setReducerClass(WCoReducer.class);
 
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
 
    boolean success = job.waitForCompletion(true);
    return success ? 0 : 1;
  }
 
  public static void main(String[] args) throws Exception {
    int exitCode = ToolRunner.run(new Configuration(), new WCo(), args);
    System.exit(exitCode);
  }
}

 

算法的核心其实就是把前词和后词同时取出来作为key加上一个value做word count,统计单词的共生频率来对文本进行聚类.看网上说k-means的很多,其实很多时候算法是根据需求走的,k-means或者模糊k均值不一定就高大上,wordcount也不一定就穷矮矬.