@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);
}
@Override
public int getPartition(TextInt key, Tuple value, int numPartitions) {
// consider only base part of key
return key.baseHashCode() % numPartitions;
}
/* (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);
String itemID = items[0];
if (isRatingFileSplit) {
// user rating
context.getCounter("Record type count", "Rating").increment(1);
boolean toInclude = true;
for (int i = 1; i < items.length; ++i) {
// all user ratings for this item
ratings = items[i].split(subFieldDelim);
// time sensitive recommendation
toInclude = true;
if (ratingTimeCutoff > 0) {
timeStamp = Long.parseLong(ratings[2]);
toInclude = timeStamp > ratingTimeCutoff;
}
// contextual recommendation
if (userRatingWithContext) {
ratingContext = ratings[3];
}
// check for min input rating threshold
inputRating = new Integer(ratings[1]);
toInclude = toInclude && inputRating > minInputRating;
if (toInclude) {
// itemID
keyOut.set(itemID, two);
// userID, rating
valOut.initialize();
if (userRatingWithContext) {
valOut.add(ratings[0], inputRating, context, two);
} else {
valOut.add(ratings[0], inputRating, two);
}
context.write(keyOut, valOut);
}
}
} else if (isRatingStatFileSplit) {
// rating stat
context.getCounter("Record type count", "Rating stat").increment(1);
int ratingStdDev = Integer.parseInt(items[STD_DEV_ORD]);
keyOut.set(itemID, one);
valOut.initialize();
valOut.add(ratingStdDev, one);
context.write(keyOut, valOut);
} else {
// item correlation
context.getCounter("Record type count", "Correlation").increment(1);
correlation = Integer.parseInt(items[2]);
correlationLength = Integer.parseInt(items[3]);
// if correlation is above min threshold
if (correlation > minCorrelation) {
// correlation of 1st item
keyOut.set(items[0], zero);
valOut.initialize();
if (linearCorrelation) {
// other itemID, correlation, intersection length (weight)
valOut.add(items[1], correlation, correlationLength, zero);
} else {
// other itemID, correlation, intersection length (weight)
valOut.add(items[1], -correlation, correlationLength, zero);
}
context.write(keyOut, valOut);
// correlation of second item
keyOut.set(items[1], zero);
valOut.initialize();
if (linearCorrelation) {
// other itemID, correlation, intersection length (weight)
valOut.add(items[0], correlation, correlationLength, zero);
} else {
// other itemID, correlation, intersection length (weight)
valOut.add(items[0], -correlation, correlationLength, zero);
}
context.write(keyOut, valOut);
}
}
}
