/**
* 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.clustering.spectral.common;
import java.util.List;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.mahout.clustering.spectral.common.MatrixDiagonalizeJob.MatrixDiagonalizeMapper;
import org.apache.mahout.clustering.spectral.common.MatrixDiagonalizeJob.MatrixDiagonalizeReducer;
import org.apache.mahout.clustering.spectral.eigencuts.EigencutsKeys;
import org.apache.mahout.common.DummyRecordWriter;
import org.apache.mahout.common.MahoutTestCase;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import org.junit.Test;
/**
* <p>The MatrixDiagonalize task is pretty simple: given a matrix,
* it sums the elements of the row, and sticks the sum in position (i, i)
* of a new matrix of identical dimensions to the original.</p>
*/
public class TestMatrixDiagonalizeJob extends MahoutTestCase {
private static final double[][] RAW = { {1, 2, 3}, {4, 5, 6}, {7, 8, 9} };
private static final int RAW_DIMENSIONS = 3;
private static double rowSum(double [] row) {
double sum = 0;
for (double r : row) {
sum += r;
}
return sum;
}
@Test
public void testMatrixDiagonalizeMapper() throws Exception {
MatrixDiagonalizeMapper mapper = new MatrixDiagonalizeMapper();
Configuration conf = new Configuration();
conf.setInt(EigencutsKeys.AFFINITY_DIMENSIONS, RAW_DIMENSIONS);
// set up the dummy writers
DummyRecordWriter<NullWritable, IntDoublePairWritable> writer =
new DummyRecordWriter<NullWritable, IntDoublePairWritable>();
Mapper<IntWritable, VectorWritable, NullWritable, IntDoublePairWritable>.Context
context = DummyRecordWriter.build(mapper, conf, writer);
// perform the mapping
for (int i = 0; i < RAW_DIMENSIONS; i++) {
RandomAccessSparseVector toAdd = new RandomAccessSparseVector(RAW_DIMENSIONS);
toAdd.assign(RAW[i]);
mapper.map(new IntWritable(i), new VectorWritable(toAdd), context);
}
// check the number of the results
assertEquals("Number of map results", RAW_DIMENSIONS,
writer.getValue(NullWritable.get()).size());
}
@Test
public void testMatrixDiagonalizeReducer() throws Exception {
MatrixDiagonalizeMapper mapper = new MatrixDiagonalizeMapper();
Configuration conf = new Configuration();
conf.setInt(EigencutsKeys.AFFINITY_DIMENSIONS, RAW_DIMENSIONS);
// set up the dummy writers
DummyRecordWriter<NullWritable, IntDoublePairWritable> mapWriter =
new DummyRecordWriter<NullWritable, IntDoublePairWritable>();
Mapper<IntWritable, VectorWritable, NullWritable, IntDoublePairWritable>.Context
mapContext = DummyRecordWriter.build(mapper, conf, mapWriter);
// perform the mapping
for (int i = 0; i < RAW_DIMENSIONS; i++) {
RandomAccessSparseVector toAdd = new RandomAccessSparseVector(RAW_DIMENSIONS);
toAdd.assign(RAW[i]);
mapper.map(new IntWritable(i), new VectorWritable(toAdd), mapContext);
}
// now perform the reduction
MatrixDiagonalizeReducer reducer = new MatrixDiagonalizeReducer();
DummyRecordWriter<NullWritable, VectorWritable> redWriter = new
DummyRecordWriter<NullWritable, VectorWritable>();
Reducer<NullWritable, IntDoublePairWritable, NullWritable, VectorWritable>.Context
redContext = DummyRecordWriter.build(reducer, conf, redWriter,
NullWritable.class, IntDoublePairWritable.class);
// only need one reduction
reducer.reduce(NullWritable.get(), mapWriter.getValue(NullWritable.get()), redContext);
// first, make sure there's only one result
List<VectorWritable> list = redWriter.getValue(NullWritable.get());
assertEquals("Only a single resulting vector", 1, list.size());
Vector v = list.get(0).get();
for (int i = 0; i < v.size(); i++) {
assertEquals("Element sum is correct", rowSum(RAW[i]), v.get(i),0.01);
}
}
}