package org.conan.myhadoop.recommend;
import java.io.IOException;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Map;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.conan.myhadoop.hdfs.HdfsDAO;
public class Step4_Update {
public static class Step4_PartialMultiplyMapper extends Mapper<LongWritable, Text, Text, Text> {
private String flag;// A同现矩阵 or B评分矩阵
@Override
protected void setup(Context context) throws IOException, InterruptedException {
FileSplit split = (FileSplit) context.getInputSplit();
flag = split.getPath().getParent().getName();// 判断读的数据集
// System.out.println(flag);
}
@Override
public void map(LongWritable key, Text values, Context context) throws IOException, InterruptedException {
String[] tokens = Recommend.DELIMITER.split(values.toString());
if (flag.equals("step3_2")) {// 同现矩阵
String[] v1 = tokens[0].split(":");
String itemID1 = v1[0];
String itemID2 = v1[1];
String num = tokens[1];
Text k = new Text(itemID1);
Text v = new Text("A:" + itemID2 + "," + num);
context.write(k, v);
// System.out.println(k.toString() + " " + v.toString());
} else if (flag.equals("step3_1")) {// 评分矩阵
String[] v2 = tokens[1].split(":");
String itemID = tokens[0];
String userID = v2[0];
String pref = v2[1];
Text k = new Text(itemID);
Text v = new Text("B:" + userID + "," + pref);
context.write(k, v);
// System.out.println(k.toString() + " " + v.toString());
}
}
}
public static class Step4_AggregateReducer extends Reducer<Text, Text, Text, Text> {
@Override
public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
System.out.println(key.toString() + ":");
Map<String, String> mapA = new HashMap<String, String>();
Map<String, String> mapB = new HashMap<String, String>();
for (Text line : values) {
String val = line.toString();
System.out.println(val);
if (val.startsWith("A:")) {
String[] kv = Recommend.DELIMITER.split(val.substring(2));
mapA.put(kv[0], kv[1]);
} else if (val.startsWith("B:")) {
String[] kv = Recommend.DELIMITER.split(val.substring(2));
mapB.put(kv[0], kv[1]);
}
}
double result = 0;
Iterator<String> iter = mapA.keySet().iterator();
while (iter.hasNext()) {
String mapk = iter.next();// itemID
int num = Integer.parseInt(mapA.get(mapk));
Iterator<String> iterb = mapB.keySet().iterator();
while (iterb.hasNext()) {
String mapkb = iterb.next();// userID
double pref = Double.parseDouble(mapB.get(mapkb));
result = num * pref;// 矩阵乘法相乘计算
Text k = new Text(mapkb);
Text v = new Text(mapk + "," + result);
context.write(k, v);
System.out.println(k.toString() + " " + v.toString());
}
}
}
}
public static void run(Map<String, String> path) throws IOException, InterruptedException, ClassNotFoundException {
JobConf conf = Recommend.config();
String input1 = path.get("Step5Input1");
String input2 = path.get("Step5Input2");
String output = path.get("Step5Output");
HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);
hdfs.rmr(output);
Job job = new Job(conf);
job.setJarByClass(Step4_Update.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setMapperClass(Step4_Update.Step4_PartialMultiplyMapper.class);
job.setReducerClass(Step4_Update.Step4_AggregateReducer.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.setInputPaths(job, new Path(input1), new Path(input2));
FileOutputFormat.setOutputPath(job, new Path(output));
job.waitForCompletion(true);
}
}