/**
* tests {@link UserVectorSplitterMapper} in the special case that some userIDs shall be excluded
*/
@Test
public void testUserVectorSplitterMapperUserExclusion() throws Exception {
Mapper<VarLongWritable, VectorWritable, VarIntWritable, VectorOrPrefWritable>.Context context =
EasyMock.createMock(Mapper.Context.class);
context.write(
EasyMock.eq(new VarIntWritable(34)), prefOfVectorOrPrefWritableMatches(123L, 0.5f));
context.write(
EasyMock.eq(new VarIntWritable(56)), prefOfVectorOrPrefWritableMatches(123L, 0.7f));
EasyMock.replay(context);
FastIDSet usersToRecommendFor = new FastIDSet();
usersToRecommendFor.add(123L);
UserVectorSplitterMapper mapper = new UserVectorSplitterMapper();
setField(mapper, "maxPrefsPerUserConsidered", 10);
setField(mapper, "usersToRecommendFor", usersToRecommendFor);
RandomAccessSparseVector vector = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
vector.set(34, 0.5);
vector.set(56, 0.7);
mapper.map(new VarLongWritable(123L), new VectorWritable(vector), context);
mapper.map(new VarLongWritable(456L), new VectorWritable(vector), context);
EasyMock.verify(context);
}
@Override
protected void map(IntWritable row, VectorWritable vectorWritable, Context ctx)
throws IOException, InterruptedException {
Vector rowVector = similarity.normalize(vectorWritable.get());
int numNonZeroEntries = 0;
double maxValue = Double.MIN_VALUE;
Iterator<Vector.Element> nonZeroElements = rowVector.iterateNonZero();
while (nonZeroElements.hasNext()) {
Vector.Element element = nonZeroElements.next();
RandomAccessSparseVector partialColumnVector =
new RandomAccessSparseVector(Integer.MAX_VALUE);
partialColumnVector.setQuick(row.get(), element.get());
ctx.write(new IntWritable(element.index()), new VectorWritable(partialColumnVector));
numNonZeroEntries++;
if (maxValue < element.get()) {
maxValue = element.get();
}
}
if (threshold != NO_THRESHOLD) {
nonZeroEntries.setQuick(row.get(), numNonZeroEntries);
maxValues.setQuick(row.get(), maxValue);
}
norms.setQuick(row.get(), similarity.norm(rowVector));
ctx.getCounter(Counters.ROWS).increment(1);
}
/** tests {@link AggregateAndRecommendReducer} */
@Test
public void testAggregateAndRecommendReducer() throws Exception {
Reducer<
VarLongWritable,
PrefAndSimilarityColumnWritable,
VarLongWritable,
RecommendedItemsWritable>
.Context
context = EasyMock.createMock(Reducer.Context.class);
context.write(
EasyMock.eq(new VarLongWritable(123L)),
recommendationsMatch(
new GenericRecommendedItem(1L, 2.8f), new GenericRecommendedItem(2L, 2.0f)));
EasyMock.replay(context);
RandomAccessSparseVector similarityColumnOne =
new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
similarityColumnOne.set(1, 0.1);
similarityColumnOne.set(2, 0.5);
RandomAccessSparseVector similarityColumnTwo =
new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
similarityColumnTwo.set(1, 0.9);
similarityColumnTwo.set(2, 0.5);
List<PrefAndSimilarityColumnWritable> values =
Arrays.asList(
new PrefAndSimilarityColumnWritable(1.0f, similarityColumnOne),
new PrefAndSimilarityColumnWritable(3.0f, similarityColumnTwo));
OpenIntLongHashMap indexItemIDMap = new OpenIntLongHashMap();
indexItemIDMap.put(1, 1L);
indexItemIDMap.put(2, 2L);
AggregateAndRecommendReducer reducer = new AggregateAndRecommendReducer();
setField(reducer, "indexItemIDMap", indexItemIDMap);
setField(reducer, "recommendationsPerUser", 3);
reducer.reduce(new VarLongWritable(123L), values, context);
EasyMock.verify(context);
}
/** tests {@link SimilarityMatrixRowWrapperMapper} */
@Test
public void testSimilarityMatrixRowWrapperMapper() throws Exception {
Mapper<IntWritable, VectorWritable, VarIntWritable, VectorOrPrefWritable>.Context context =
EasyMock.createMock(Mapper.Context.class);
context.write(
EasyMock.eq(new VarIntWritable(12)),
vectorOfVectorOrPrefWritableMatches(MathHelper.elem(34, 0.5), MathHelper.elem(56, 0.7)));
EasyMock.replay(context);
RandomAccessSparseVector vector = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
vector.set(12, 1.0);
vector.set(34, 0.5);
vector.set(56, 0.7);
new SimilarityMatrixRowWrapperMapper()
.map(new IntWritable(12), new VectorWritable(vector), context);
EasyMock.verify(context);
}
@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<>();
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<>();
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<>();
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);
}
}
