/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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.apache.mahout.math.hadoop.stochasticsvd;
import java.io.IOException;
import java.util.Iterator;
import org.apache.commons.lang.Validate;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.SequenceFile.CompressionType;
import org.apache.hadoop.io.Writable;
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.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
/**
* Job that accumulates Y'Y output
*
*/
public class YtYJob {
public static final String PROP_OMEGA_SEED = "ssvd.omegaseed";
public static final String PROP_K = "ssvd.k";
public static final String PROP_P = "ssvd.p";
// we have single output, so we use standard output
public static final String OUTPUT_YtY = "part-";
private YtYJob() {
}
public static class YtYMapper extends
Mapper<Writable, VectorWritable, IntWritable, VectorWritable> {
private int kp;
private Omega omega;
private UpperTriangular mYtY;
/*
* we keep yRow in a dense form here but keep an eye not to dense up while
* doing YtY products. I am not sure that sparse vector would create much
* performance benefits since we must to assume that y would be more often
* dense than sparse, so for bulk dense operations that would perform
* somewhat better than a RandomAccessSparse vector frequent updates.
*/
private Vector yRow;
@Override
protected void setup(Context context) throws IOException,
InterruptedException {
int k = context.getConfiguration().getInt(PROP_K, -1);
int p = context.getConfiguration().getInt(PROP_P, -1);
Validate.isTrue(k > 0, "invalid k parameter");
Validate.isTrue(p > 0, "invalid p parameter");
kp = k + p;
long omegaSeed = Long.parseLong(context.getConfiguration()
.get(PROP_OMEGA_SEED));
omega = new Omega(omegaSeed, k, p);
mYtY = new UpperTriangular(kp);
// see which one works better!
// yRow = new RandomAccessSparseVector(kp);
yRow = new DenseVector(kp);
}
@Override
protected void map(Writable key, VectorWritable value, Context context)
throws IOException, InterruptedException {
omega.computeYRow(value.get(), yRow);
// compute outer product update for YtY
if (yRow.isDense()) {
for (int i = 0; i < kp; i++) {
double yi;
if ((yi = yRow.getQuick(i)) == 0.0) {
continue; // avoid densing up here unnecessarily
}
for (int j = i; j < kp; j++) {
double yj;
if ((yj = yRow.getQuick(j)) != 0.0) {
mYtY.setQuick(i, j, mYtY.getQuick(i, j) + yi * yj);
}
}
}
} else {
/*
* the disadvantage of using sparse vector (aside from the fact that we
* are creating some short-lived references) here is that we obviously
* do two times more iterations then necessary if y row is pretty dense.
*/
for (Iterator<Vector.Element> iterI = yRow.iterateNonZero(); iterI
.hasNext();) {
Vector.Element eli = iterI.next();
int i = eli.index();
for (Iterator<Vector.Element> iterJ = yRow.iterateNonZero(); iterJ
.hasNext();) {
Vector.Element elj = iterJ.next();
int j = elj.index();
if (j < i) {
continue;
}
mYtY.setQuick(i, j, mYtY.getQuick(i, j) + eli.get() * elj.get());
}
}
}
}
@Override
protected void cleanup(Context context) throws IOException,
InterruptedException {
context.write(new IntWritable(context.getTaskAttemptID().getTaskID()
.getId()), new VectorWritable(new DenseVector(mYtY.getData())));
}
}
public static class YtYReducer extends
Reducer<IntWritable, VectorWritable, IntWritable, VectorWritable> {
private final VectorWritable accum = new VectorWritable();
private DenseVector acc;
@Override
protected void setup(Context context) throws IOException,
InterruptedException {
int k = context.getConfiguration().getInt(PROP_K, -1);
int p = context.getConfiguration().getInt(PROP_P, -1);
Validate.isTrue(k > 0, "invalid k parameter");
Validate.isTrue(p > 0, "invalid p parameter");
accum.set(acc = new DenseVector(k + p));
}
@Override
protected void cleanup(Context context) throws IOException,
InterruptedException {
context.write(new IntWritable(), accum);
}
@Override
protected void reduce(IntWritable key, Iterable<VectorWritable> values,
Context arg2) throws IOException, InterruptedException {
for (VectorWritable vw : values) {
acc.addAll(vw.get());
}
}
}
public static void run(Configuration conf, Path[] inputPaths,
Path outputPath, int k, int p, long seed)
throws ClassNotFoundException, InterruptedException, IOException {
Job job = new Job(conf);
job.setJobName("YtY-job");
job.setJarByClass(YtYJob.class);
job.setInputFormatClass(SequenceFileInputFormat.class);
FileInputFormat.setInputPaths(job, inputPaths);
FileOutputFormat.setOutputPath(job, outputPath);
SequenceFileOutputFormat.setOutputCompressionType(job,
CompressionType.BLOCK);
job.setMapOutputKeyClass(IntWritable.class);
job.setMapOutputValueClass(VectorWritable.class);
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(VectorWritable.class);
job.setMapperClass(YtYMapper.class);
job.getConfiguration().setLong(PROP_OMEGA_SEED, seed);
job.getConfiguration().setInt(PROP_K, k);
job.getConfiguration().setInt(PROP_P, p);
/*
* we must reduce to just one matrix which means we need only one reducer.
* But it's ok since each mapper outputs only one vector (a packed
* UpperTriangular) so even if there're thousands of mappers, one reducer
* should cope just fine.
*/
job.setNumReduceTasks(1);
job.submit();
job.waitForCompletion(false);
if (!job.isSuccessful()) {
throw new IOException("YtY job unsuccessful.");
}
}
}