To achieve that I use NamedVectors.
As you know, before doing any clusterization with your data, you have to vectorize it.
This means that you have to transform your data into Mahout vectors, because that is the
kind of data that clusterization algoritms work with.
Vectorization process will depend on the nature of your data, i.e. vectorizing text is not the same to
vectorize numerical values.
Your data seems to be easily vectorizable, since it only have an ID and 4 numerical values.
You could write a Hadoop Job that takes your input data, for example, as a CSV file,
and outputs a SequenceFile with your data already vectorized.
Then, you apply the Mahout clustering algorithms to this input and you will keep the ID (vector name) of each vector in the clustering results.
An example job to vectorize your data could be implemented with the following classes:
public class DenseVectorizationDriver extends Configured implements Tool{
@Override
public int run(String[] args) throws Exception {
if (args.length != 2) {
System.err.printf("Usage: %s [generic options] <input> <output>\n", getClass().getSimpleName());
ToolRunner.printGenericCommandUsage(System.err); return -1;
}
Job job = new Job(getConf(), "Create Dense Vectors from CSV input");
job.setJarByClass(DenseVectorizationDriver.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(DenseVectorizationMapper.class);
job.setReducerClass(DenseVectorizationReducer.class);
job.setOutputKeyClass(LongWritable.class);
job.setOutputValueClass(VectorWritable.class);
job.setOutputFormatClass(SequenceFileOutputFormat.class);
return job.waitForCompletion(true) ? 0 : 1;
}
}
public class DenseVectorizationMapper extends Mapper<LongWritable, Text, LongWritable, VectorWritable>{
/*
* This mapper class takes the input from a CSV file whose fields are separated by TAB and emits
* the same key it receives (useless in this case) and a NamedVector as value.
* The "name" of the NamedVector is the ID of each row.
*/
@Override
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
System.out.println("LINE: "+line);
String[] lineParts = line.split("\t", -1);
String id = lineParts[0];
//you should do some checks here to assure that this piece of data is correct
Vector vector = new DenseVector(lineParts.length -1);
for (int i = 1; i < lineParts.length -1; i++){
String strValue = lineParts[i];
System.out.println("VALUE: "+strValue);
vector.set(i, Double.parseDouble(strValue));
}
vector = new NamedVector(vector, id);
context.write(key, new VectorWritable(vector));
}
}
public class DenseVectorizationReducer extends Reducer<LongWritable, VectorWritable, LongWritable, VectorWritable>{
/*
* This reducer simply writes the output without doing any computation.
* Maybe it would be better to define this hadoop job without reduce phase.
*/
@Override
public void reduce(LongWritable key, Iterable<VectorWritable> values, Context context) throws IOException, InterruptedException{
VectorWritable writeValue = values.iterator().next();
context.write(key, writeValue);
}
}
