/*
* Sifarish: Recommendation Engine
* Author: Pranab Ghosh
*
* Licensed under the Apache License, Version 2.0 (the "License"); you
* may not use this file except in compliance with the License. You may
* obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied. See the License for the specific language governing
* permissions and limitations under the License.
*/
package org.sifarish.social;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
import org.apache.hadoop.mapred.FileSplit;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.chombo.util.TextInt;
import org.chombo.util.Tuple;
import org.chombo.util.Utility;
/**
* Predicts rating for an user and item. based on another item the user has rated and the
* correlation between the items
* @author pranab
*
*/
public class RatingPredictor extends Configured implements Tool{
@Override
public int run(String[] args) throws Exception {
Job job = new Job(getConf());
String jobName = "Rating predictor MR";
job.setJobName(jobName);
job.setJarByClass(RatingPredictor.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(RatingPredictor.PredictionMapper.class);
job.setReducerClass(RatingPredictor.PredictorReducer.class);
job.setMapOutputKeyClass(TextInt.class);
job.setMapOutputValueClass(Tuple.class);
job.setOutputKeyClass(NullWritable.class);
job.setOutputValueClass(Text.class);
job.setGroupingComparatorClass(ItemIdGroupComprator.class);
job.setPartitionerClass(ItemIdPartitioner.class);
Utility.setConfiguration(job.getConfiguration());
int numReducer = job.getConfiguration().getInt("rap.num.reducer", -1);
numReducer = -1 == numReducer ? job.getConfiguration().getInt("num.reducer", 1) : numReducer;
job.setNumReduceTasks(numReducer);
int status = job.waitForCompletion(true) ? 0 : 1;
return status;
}
/**
* @author pranab
*
*/
public static class PredictionMapper extends Mapper<LongWritable, Text, TextInt, Tuple> {
private String fieldDelim;
private String subFieldDelim;
private boolean isRatingFileSplit;
private TextInt keyOut = new TextInt();
private Tuple valOut = new Tuple();
private String[] ratings;
private Integer one = 1;
private Integer zero = 0;
private boolean linearCorrelation;
/* (non-Javadoc)
* @see org.apache.hadoop.mapreduce.Mapper#setup(org.apache.hadoop.mapreduce.Mapper.Context)
*/
protected void setup(Context context) throws IOException, InterruptedException {
fieldDelim = context.getConfiguration().get("field.delim", ",");
subFieldDelim = context.getConfiguration().get("field.delim", ":");
String ratingFilePrefix = context.getConfiguration().get("rap.rating.file.prefix", "rating");
isRatingFileSplit = ((FileSplit)context.getInputSplit()).getPath().getName().startsWith(ratingFilePrefix);
linearCorrelation = context.getConfiguration().getBoolean("rap.correlation.linear", true);
}
/* (non-Javadoc)
* @see org.apache.hadoop.mapreduce.Mapper#map(KEYIN, VALUEIN, org.apache.hadoop.mapreduce.Mapper.Context)
*/
@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String[] items = value.toString().split(fieldDelim);
if (isRatingFileSplit) {
//user rating
String itemID = items[0];
for (int i = 1; i < items.length; ++i) {
valOut.initialize();
ratings = items[i].split(subFieldDelim);
keyOut.set(itemID, 1);
valOut.add(ratings[0], new Integer(ratings[1]), one);
context.write(keyOut, valOut);
}
} else {
//rating correlation
keyOut.set(items[0], 0);
valOut.add(items[1], new Integer(items[2]), new Integer(items[3]), zero);
context.write(keyOut, valOut);
keyOut.set(items[1], 0);
if (linearCorrelation) {
valOut.add(items[0], new Integer( items[2]), new Integer(items[3]), zero);
} else {
valOut.add(items[0], new Integer("-" + items[2]), new Integer(items[3]), zero);
}
context.write(keyOut, valOut);
}
}
}
/**
* @author pranab
*
*/
public static class PredictorReducer extends Reducer<TextInt, Tuple, NullWritable, Text> {
private String fieldDelim;
private Text valueOut = new Text();
private List<Tuple> avRatingDiffs = new ArrayList<Tuple>();
private boolean linearCorrelation;
private int correlationScale;
/* (non-Javadoc)
* @see org.apache.hadoop.mapreduce.Reducer#setup(org.apache.hadoop.mapreduce.Reducer.Context)
*/
protected void setup(Context context) throws IOException, InterruptedException {
fieldDelim = context.getConfiguration().get("field.delim", ",");
linearCorrelation = context.getConfiguration().getBoolean("rap.correlation.linear", true);
correlationScale = context.getConfiguration().getInt("rap.correlation.linear.scale", 1000);
}
/* (non-Javadoc)
* @see org.apache.hadoop.mapreduce.Reducer#reduce(KEYIN, java.lang.Iterable, org.apache.hadoop.mapreduce.Reducer.Context)
*/
protected void reduce(TextInt key, Iterable<Tuple> values, Context context)
throws IOException, InterruptedException {
avRatingDiffs.clear();
for(Tuple value : values) {
if ( ((Integer)value.get(value.getSize()-1)) == 0) {
avRatingDiffs.add(value);
} else {
if (!avRatingDiffs.isEmpty()) {
String userID = value.getString(0);
int rating = value.getInt(1);
for (Tuple ratingDiffTup : avRatingDiffs) {
String itemID = ratingDiffTup.getString(0);
int ratingCorr = ratingDiffTup.getInt(1);
int weight = ratingDiffTup.getInt(2);
int predRating = linearCorrelation? (rating * ratingCorr) / correlationScale : rating + ratingCorr;
valueOut.set(userID + fieldDelim + itemID + fieldDelim + predRating + fieldDelim + weight);
context.write(NullWritable.get(), valueOut);
}
}
}
}
}
}
/**
* @author pranab
*
*/
public static class ItemIdPartitioner extends Partitioner<TextInt, Tuple> {
@Override
public int getPartition(TextInt key, Tuple value, int numPartitions) {
//consider only base part of key
return key.baseHashCode()% numPartitions;
}
}
/**
* @author pranab
*
*/
public static class ItemIdGroupComprator extends WritableComparator {
protected ItemIdGroupComprator() {
super(TextInt.class, true);
}
@Override
public int compare(WritableComparable w1, WritableComparable w2) {
//consider only the base part of the key
TextInt t1 = ((TextInt)w1);
TextInt t2 = ((TextInt)w2);
return t1.baseCompareTo(t2);
}
}
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
int exitCode = ToolRunner.run(new RatingPredictor(), args);
System.exit(exitCode);
}
}