使用MapReduce实现Bayes算法

本文主要是介绍使用MapReduce实现Bayes算法，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

代码如下：

NBayes.conf

4 cl1 cl2 cl3 cl4
3 p1 12 p2 16 p3 17

NBayes.train

cl1 5 6 7
cl2 3 8 4
cl1 2 5 2
cl3 7 8 7
cl4 3 8 2
cl4 9 2 7
cl2 1 8 5
cl5 2 9 4
cl3 10 3 4
cl1 4 5 6
cl3 4 6 7

NBayes.test

1 5 6 7
2 1 8 5
3 2 9 4
4 10 3 4
5 4 5 6
6 3 8 4
7 2 5 2
8 7 8 7
9 3 8 2
10 9 2 7
11 4 6 7

package naivebayes;

import java.util.ArrayList;

public class NaiveBayesConf {
public int dimen;
public int class_num;
public ArrayList<String> classNames;
public ArrayList<String> proNames;
public ArrayList<Integer> proRanges;

public NaiveBayesConf() {
dimen = class_num = 0;
classNames = new ArrayList<String>();
proNames = new ArrayList<String>();
proRanges = new ArrayList<Integer>();
}

public void ReadNaiveBayesConf(String file, Configuration conf)
throws Exception {
Path conf_path = new Path(file);
FileSystem hdfs = conf_path.getFileSystem(conf);
FSDataInputStream fsdt = hdfs.open(conf_path);
Scanner scan = new Scanner(fsdt);
String str = scan.nextLine();
String[] vals = str.split(" ");

class_num = Integer.parseInt(vals[0]);

int i;

for (i = 1; i < vals.length; i++) {
classNames.add(vals[i]);
}

str = scan.nextLine();
vals = str.split(" ");
dimen = Integer.parseInt(vals[0]);

for (i = 1; i < vals.length; i += 2) {
proNames.add(vals[i]);
proRanges.add(new Integer(vals[i + 1]));
}
fsdt.close();
scan.close();
}
}

package naivebayes;

import org.apache.hadoop.conf.Configuration;

public class NaiveBayesMain {
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args)
.getRemainingArgs();
FileSystem fs = FileSystem.get(conf);
Path path_train, path_temp, path_test, path_out;
if (otherArgs.length != 5) {
System.err
.println("Usage: NaiveBayesMain <dfs_path> <conf> <train> <test> <out>");
System.exit(2);
}

conf.set("conf", otherArgs[0] + "/" + otherArgs[1]);
conf.set("train", otherArgs[0] + "/" + otherArgs[2]);
conf.set("test", otherArgs[0] + "/" + otherArgs[3]);
conf.set("output", otherArgs[0] + "/" + otherArgs[4]);

put2HDFS(otherArgs[1], otherArgs[0] + "/" + otherArgs[1], conf);
put2HDFS(otherArgs[2], otherArgs[0] + "/" + otherArgs[2], conf);
put2HDFS(otherArgs[3], otherArgs[0] + "/" + otherArgs[3], conf);

path_train = new Path(otherArgs[0] + "/" + otherArgs[2]);
path_temp = new Path(otherArgs[0] + "/" + otherArgs[2] + ".train1");
path_test = new Path(otherArgs[0] + "/" + otherArgs[3]);
path_out = new Path(otherArgs[0] + "/" + otherArgs[4]);

{
Job job_train = new Job(conf, "naive bayse training");
job_train.setJarByClass(NaiveBayesMain.class);
job_train.setMapperClass(NaiveBayesTrain.TrainMapper.class);
job_train.setCombinerClass(NaiveBayesTrain.TrainReducer.class);
job_train.setReducerClass(NaiveBayesTrain.TrainReducer.class);
job_train.setOutputKeyClass(Text.class);
job_train.setOutputValueClass(IntWritable.class);

FileInputFormat.setInputPaths(job_train, path_train);
if (fs.exists(path_temp))
fs.delete(path_temp, true);
FileOutputFormat.setOutputPath(job_train, path_temp);
if (job_train.waitForCompletion(true) == false)
System.exit(1);

conf.set("train_result", otherArgs[0] + "/" + otherArgs[2]
+ ".train1");
}
{
Job job_test = new Job(conf, "naive bayse testing");
job_test.setJarByClass(NaiveBayesTest.class);
job_test.setMapperClass(NaiveBayesTest.TestMapper.class);
job_test.setOutputKeyClass(Text.class);
job_test.setOutputValueClass(Text.class);

FileInputFormat.setInputPaths(job_test, path_test);
if (fs.exists(path_out))
fs.delete(path_out, true);
FileOutputFormat.setOutputPath(job_test, path_out);
if (job_test.waitForCompletion(true) == false)
System.exit(1);
fs.delete(path_temp, true);
}

// getFromHDFS(otherArgs[0] + "/" + otherArgs[4], ".", conf);

fs.close();
System.exit(0);
}

public static void put2HDFS(String src, String dst, Configuration conf)
throws Exception {
Path dstPath = new Path(dst);
FileSystem hdfs = dstPath.getFileSystem(conf);

hdfs.copyFromLocalFile(false, true, new Path(src), new Path(dst));

}

public static void getFromHDFS(String src, String dst, Configuration conf)
throws Exception {
Path dstPath = new Path(dst);
FileSystem lfs = dstPath.getFileSystem(conf);
String temp[] = src.split("/");
Path ptemp = new Path(temp[temp.length - 1]);
if (lfs.exists(ptemp))
;
lfs.delete(ptemp, true);
lfs.copyToLocalFile(true, new Path(src), dstPath);

}
}

package naivebayes;

import java.util.Scanner;

public class NaiveBayesTrain {
public static class TrainMapper extends
Mapper<Object, Text, Text, IntWritable> {
public NaiveBayesConf nBConf;
private final static IntWritable one = new IntWritable(1);
private Text word;

public void setup(Context context) {
try {
nBConf = new NaiveBayesConf();
Configuration conf = context.getConfiguration();
nBConf.ReadNaiveBayesConf(conf.get("conf"), conf);
} catch (Exception ex) {
ex.printStackTrace();
System.exit(1);
}
System.out.println("setup");
}

public void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
Scanner scan = new Scanner(value.toString());
String str, vals[], temp;
int i;
word = new Text();
while (scan.hasNextLine()) {
str = scan.nextLine();
vals = str.split(" ");
word.set(vals[0]);
context.write(word, one);
for (i = 1; i < vals.length; i++) {
word = new Text();
temp = vals[0] + "#" + nBConf.proNames.get(i - 1);
temp += "#" + vals[i];
word.set(temp);
context.write(word, one);
}
}
}
}

public static class TrainReducer extends
Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
}

package naivebayes;

import java.io.BufferedReader;

public class NaiveBayesTrainData {
public HashMap<String, Integer> freq;

public NaiveBayesTrainData() {
freq = new HashMap<String, Integer>();
}

public void getData(String file, Configuration conf) throws IOException {
int i;
Path data_path = new Path(file);
Path file_path;
String temp[], line;
FileSystem hdfs = data_path.getFileSystem(conf);
FileStatus[] status = hdfs.listStatus(data_path);

for (i = 0; i < status.length; i++) {
file_path = status[i].getPath();
if (hdfs.getFileStatus(file_path).isDir() == true)
continue;
line = file_path.toString();
temp = line.split("/");
if (temp[temp.length - 1].substring(0, 5).equals("part-") == false)
continue;
System.err.println(line);
FSDataInputStream fin = hdfs.open(file_path);
InputStreamReader inr = new InputStreamReader(fin);
BufferedReader bfr = new BufferedReader(inr);
while ((line = bfr.readLine()) != null) {
String res[] = line.split("\t");
freq.put(res[0], new Integer(res[1]));
System.out.println(line);
}
bfr.close();
inr.close();
fin.close();
}
}

}

package naivebayes;

import java.util.Scanner;

public class NaiveBayesTest {
public static class TestMapper extends Mapper<Object, Text, Text, Text> {
public NaiveBayesConf nBConf;
public NaiveBayesTrainData nBTData;

public void setup(Context context) {
try {
Configuration conf = context.getConfiguration();

nBConf = new NaiveBayesConf();
nBConf.ReadNaiveBayesConf(conf.get("conf"), conf);
nBTData = new NaiveBayesTrainData();
nBTData.getData(conf.get("train_result"), conf);
} catch (Exception ex) {
ex.printStackTrace();
System.exit(1);
}
}

public void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
Scanner scan = new Scanner(value.toString());
String str, vals[], temp;
int i, j, k, fxyi, fyi, fyij, maxf, idx;
Text id;
Text cls;

while (scan.hasNextLine()) {
str = scan.nextLine();
vals = str.split(" ");
maxf = -100;
idx = -1;
for (i = 0; i < nBConf.class_num; i++) {
fxyi = 1;
String cl = nBConf.classNames.get(i);
Integer integer = nBTData.freq.get(cl);
if (integer == null)
fyi = 0;
else
fyi = integer.intValue();
for (j = 1; j < vals.length; j++) {
temp = cl + "#" + nBConf.proNames.get(j - 1) + "#"
+ vals[j];

integer = nBTData.freq.get(temp);
if (integer == null)
fyij = 0;
else
fyij = integer.intValue();
fxyi = fxyi * fyij;
}
if (fyi * fxyi > maxf) {
maxf = fyi * fxyi;
idx = i;
}
}
id = new Text(vals[0]);
cls = new Text(nBConf.classNames.get(idx));
context.write(id, cls);
}
}
}
}