org.apache.mahout.clustering.spectral.TestMatrixDiagonalizeJob.java Source code

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Here is the source code for org.apache.mahout.clustering.spectral.TestMatrixDiagonalizeJob.java

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/**
 * 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;

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.MatrixDiagonalizeJob.MatrixDiagonalizeMapper;
import org.apache.mahout.clustering.spectral.MatrixDiagonalizeJob.MatrixDiagonalizeReducer;
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 = getConfiguration();
        conf.setInt(Keys.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 = getConfiguration();
        conf.setInt(Keys.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);
        }
    }
}