/**
* 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.cf.taste.hadoop.item;
import java.io.File;
import java.io.IOException;
import java.util.Arrays;
import java.util.Collection;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
import com.google.common.collect.Maps;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Counter;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.mahout.cf.taste.hadoop.EntityPrefWritable;
import org.apache.mahout.cf.taste.hadoop.RecommendedItemsWritable;
import org.apache.mahout.cf.taste.hadoop.TasteHadoopUtils;
import org.apache.mahout.cf.taste.hadoop.ToItemPrefsMapper;
import org.apache.mahout.cf.taste.impl.TasteTestCase;
import org.apache.mahout.cf.taste.impl.common.FastIDSet;
import org.apache.mahout.cf.taste.impl.recommender.GenericRecommendedItem;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.common.iterator.FileLineIterable;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.VarIntWritable;
import org.apache.mahout.math.VarLongWritable;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.math.hadoop.MathHelper;
import org.apache.mahout.math.hadoop.similarity.cooccurrence.measures.CooccurrenceCountSimilarity;
import org.apache.mahout.math.hadoop.similarity.cooccurrence.measures.TanimotoCoefficientSimilarity;
import org.apache.mahout.math.map.OpenIntLongHashMap;
import org.easymock.IArgumentMatcher;
import org.easymock.EasyMock;
import org.junit.Test;
public class RecommenderJobTest extends TasteTestCase {
/**
* tests {@link ItemIDIndexMapper}
*/
@Test
public void testItemIDIndexMapper() throws Exception {
Mapper<LongWritable,Text, VarIntWritable, VarLongWritable>.Context context =
EasyMock.createMock(Mapper.Context.class);
context.write(new VarIntWritable(TasteHadoopUtils.idToIndex(789L)), new VarLongWritable(789L));
EasyMock.replay(context);
new ItemIDIndexMapper().map(new LongWritable(123L), new Text("456,789,5.0"), context);
EasyMock.verify(context);
}
/**
* tests {@link ItemIDIndexReducer}
*/
@Test
public void testItemIDIndexReducer() throws Exception {
Reducer<VarIntWritable, VarLongWritable, VarIntWritable,VarLongWritable>.Context context =
EasyMock.createMock(Reducer.Context.class);
context.write(new VarIntWritable(123), new VarLongWritable(45L));
EasyMock.replay(context);
new ItemIDIndexReducer().reduce(new VarIntWritable(123), Arrays.asList(new VarLongWritable(67L),
new VarLongWritable(89L), new VarLongWritable(45L)), context);
EasyMock.verify(context);
}
/**
* tests {@link ToItemPrefsMapper}
*/
@Test
public void testToItemPrefsMapper() throws Exception {
Mapper<LongWritable,Text, VarLongWritable,VarLongWritable>.Context context =
EasyMock.createMock(Mapper.Context.class);
context.write(new VarLongWritable(12L), new EntityPrefWritable(34L, 1.0f));
context.write(new VarLongWritable(56L), new EntityPrefWritable(78L, 2.0f));
EasyMock.replay(context);
ToItemPrefsMapper mapper = new ToItemPrefsMapper();
mapper.map(new LongWritable(123L), new Text("12,34,1"), context);
mapper.map(new LongWritable(456L), new Text("56,78,2"), context);
EasyMock.verify(context);
}
/**
* tests {@link ToItemPrefsMapper} using boolean data
*/
@Test
public void testToItemPrefsMapperBooleanData() throws Exception {
Mapper<LongWritable,Text, VarLongWritable,VarLongWritable>.Context context =
EasyMock.createMock(Mapper.Context.class);
context.write(new VarLongWritable(12L), new VarLongWritable(34L));
context.write(new VarLongWritable(56L), new VarLongWritable(78L));
EasyMock.replay(context);
ToItemPrefsMapper mapper = new ToItemPrefsMapper();
setField(mapper, "booleanData", true);
mapper.map(new LongWritable(123L), new Text("12,34"), context);
mapper.map(new LongWritable(456L), new Text("56,78"), context);
EasyMock.verify(context);
}
/**
* tests {@link ToUserVectorsReducer}
*/
@Test
public void testToUserVectorReducer() throws Exception {
Reducer<VarLongWritable,VarLongWritable,VarLongWritable,VectorWritable>.Context context =
EasyMock.createMock(Reducer.Context.class);
Counter userCounters = EasyMock.createMock(Counter.class);
EasyMock.expect(context.getCounter(ToUserVectorsReducer.Counters.USERS)).andReturn(userCounters);
userCounters.increment(1);
context.write(EasyMock.eq(new VarLongWritable(12L)), MathHelper.vectorMatches(
MathHelper.elem(TasteHadoopUtils.idToIndex(34L), 1.0), MathHelper.elem(TasteHadoopUtils.idToIndex(56L), 2.0)));
EasyMock.replay(context, userCounters);
Collection<VarLongWritable> varLongWritables = new LinkedList<VarLongWritable>();
varLongWritables.add(new EntityPrefWritable(34L, 1.0f));
varLongWritables.add(new EntityPrefWritable(56L, 2.0f));
new ToUserVectorsReducer().reduce(new VarLongWritable(12L), varLongWritables, context);
EasyMock.verify(context, userCounters);
}
/**
* tests {@link ToUserVectorsReducer} using boolean data
*/
@Test
public void testToUserVectorReducerWithBooleanData() throws Exception {
Reducer<VarLongWritable,VarLongWritable,VarLongWritable,VectorWritable>.Context context =
EasyMock.createMock(Reducer.Context.class);
Counter userCounters = EasyMock.createMock(Counter.class);
EasyMock.expect(context.getCounter(ToUserVectorsReducer.Counters.USERS)).andReturn(userCounters);
userCounters.increment(1);
context.write(EasyMock.eq(new VarLongWritable(12L)), MathHelper.vectorMatches(
MathHelper.elem(TasteHadoopUtils.idToIndex(34L), 1.0), MathHelper.elem(TasteHadoopUtils.idToIndex(56L), 1.0)));
EasyMock.replay(context, userCounters);
new ToUserVectorsReducer().reduce(new VarLongWritable(12L), Arrays.asList(new VarLongWritable(34L),
new VarLongWritable(56L)), context);
EasyMock.verify(context, userCounters);
}
/**
* 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);
}
/**
* verifies the {@link Vector} included in a {@link VectorOrPrefWritable}
*/
private static VectorOrPrefWritable vectorOfVectorOrPrefWritableMatches(final Vector.Element... elements) {
EasyMock.reportMatcher(new IArgumentMatcher() {
@Override
public boolean matches(Object argument) {
if (argument instanceof VectorOrPrefWritable) {
Vector v = ((VectorOrPrefWritable) argument).getVector();
return MathHelper.consistsOf(v, elements);
}
return false;
}
@Override
public void appendTo(StringBuffer buffer) {}
});
return null;
}
/**
* tests {@link UserVectorSplitterMapper}
*/
@Test
public void testUserVectorSplitterMapper() 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);
UserVectorSplitterMapper mapper = new UserVectorSplitterMapper();
setField(mapper, "maxPrefsPerUserConsidered", 10);
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);
EasyMock.verify(context);
}
/**
* verifies a preference in a {@link VectorOrPrefWritable}
*/
private static VectorOrPrefWritable prefOfVectorOrPrefWritableMatches(final long userID, final float prefValue) {
EasyMock.reportMatcher(new IArgumentMatcher() {
@Override
public boolean matches(Object argument) {
if (argument instanceof VectorOrPrefWritable) {
VectorOrPrefWritable pref = (VectorOrPrefWritable) argument;
return pref.getUserID() == userID && pref.getValue() == prefValue;
}
return false;
}
@Override
public void appendTo(StringBuffer buffer) {}
});
return null;
}
/**
* 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);
}
/**
* tests {@link UserVectorSplitterMapper} in the special case that the number of preferences to be considered
* is less than the number of available preferences
*/
@Test
public void testUserVectorSplitterMapperOnlySomePrefsConsidered() throws Exception {
Mapper<VarLongWritable,VectorWritable, VarIntWritable,VectorOrPrefWritable>.Context context =
EasyMock.createMock(Mapper.Context.class);
context.write(EasyMock.eq(new VarIntWritable(34)), prefOfVectorOrPrefWritableMatchesNaN(123L));
context.write(EasyMock.eq(new VarIntWritable(56)), prefOfVectorOrPrefWritableMatches(123L, 0.7f));
EasyMock.replay(context);
UserVectorSplitterMapper mapper = new UserVectorSplitterMapper();
setField(mapper, "maxPrefsPerUserConsidered", 1);
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);
EasyMock.verify(context);
}
/**
* verifies that a preference value is NaN in a {@link VectorOrPrefWritable}
*/
private static VectorOrPrefWritable prefOfVectorOrPrefWritableMatchesNaN(final long userID) {
EasyMock.reportMatcher(new IArgumentMatcher() {
@Override
public boolean matches(Object argument) {
if (argument instanceof VectorOrPrefWritable) {
VectorOrPrefWritable pref = (VectorOrPrefWritable) argument;
return pref.getUserID() == userID && Float.isNaN(pref.getValue());
}
return false;
}
@Override
public void appendTo(StringBuffer buffer) {}
});
return null;
}
/**
* tests {@link ToVectorAndPrefReducer}
*/
@Test
public void testToVectorAndPrefReducer() throws Exception {
Reducer<VarIntWritable,VectorOrPrefWritable,VarIntWritable,VectorAndPrefsWritable>.Context context =
EasyMock.createMock(Reducer.Context.class);
context.write(EasyMock.eq(new VarIntWritable(1)), vectorAndPrefsWritableMatches(Arrays.asList(123L, 456L),
Arrays.asList(1.0f, 2.0f), MathHelper.elem(3, 0.5), MathHelper.elem(7, 0.8)));
EasyMock.replay(context);
Vector similarityColumn = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
similarityColumn.set(3, 0.5);
similarityColumn.set(7, 0.8);
VectorOrPrefWritable itemPref1 = new VectorOrPrefWritable(123L, 1.0f);
VectorOrPrefWritable itemPref2 = new VectorOrPrefWritable(456L, 2.0f);
VectorOrPrefWritable similarities = new VectorOrPrefWritable(similarityColumn);
new ToVectorAndPrefReducer().reduce(new VarIntWritable(1), Arrays.asList(itemPref1, itemPref2, similarities),
context);
EasyMock.verify(context);
}
/**
* verifies a {@link VectorAndPrefsWritable}
*/
private static VectorAndPrefsWritable vectorAndPrefsWritableMatches(final List<Long> userIDs,
final List<Float> prefValues, final Vector.Element... elements) {
EasyMock.reportMatcher(new IArgumentMatcher() {
@Override
public boolean matches(Object argument) {
if (argument instanceof VectorAndPrefsWritable) {
VectorAndPrefsWritable vectorAndPrefs = (VectorAndPrefsWritable) argument;
if (!vectorAndPrefs.getUserIDs().equals(userIDs)) {
return false;
}
if (!vectorAndPrefs.getValues().equals(prefValues)) {
return false;
}
return MathHelper.consistsOf(vectorAndPrefs.getVector(), elements);
}
return false;
}
@Override
public void appendTo(StringBuffer buffer) {}
});
return null;
}
/**
* tests {@link ToVectorAndPrefReducer} in the error case that two similarity column vectors a supplied for the same
* item (which should never happen)
*/
@Test
public void testToVectorAndPrefReducerExceptionOn2Vectors() throws Exception {
Reducer<VarIntWritable,VectorOrPrefWritable,VarIntWritable,VectorAndPrefsWritable>.Context context =
EasyMock.createMock(Reducer.Context.class);
EasyMock.replay(context);
Vector similarityColumn1 = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
Vector similarityColumn2 = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
VectorOrPrefWritable similarities1 = new VectorOrPrefWritable(similarityColumn1);
VectorOrPrefWritable similarities2 = new VectorOrPrefWritable(similarityColumn2);
try {
new ToVectorAndPrefReducer().reduce(new VarIntWritable(1), Arrays.asList(similarities1, similarities2), context);
fail();
} catch (IllegalStateException e) {
// good
}
EasyMock.verify(context);
}
/**
* tests {@link org.apache.mahout.cf.taste.hadoop.item.ItemFilterMapper}
*/
@Test
public void testItemFilterMapper() throws Exception {
Mapper<LongWritable,Text,VarLongWritable,VarLongWritable>.Context context =
EasyMock.createMock(Mapper.Context.class);
context.write(new VarLongWritable(34L), new VarLongWritable(12L));
context.write(new VarLongWritable(78L), new VarLongWritable(56L));
EasyMock.replay(context);
ItemFilterMapper mapper = new ItemFilterMapper();
mapper.map(null, new Text("12,34"), context);
mapper.map(null, new Text("56,78"), context);
EasyMock.verify(context);
}
/**
* tests {@link org.apache.mahout.cf.taste.hadoop.item.ItemFilterAsVectorAndPrefsReducer}
*/
@Test
public void testItemFilterAsVectorAndPrefsReducer() throws Exception {
Reducer<VarLongWritable,VarLongWritable,VarIntWritable,VectorAndPrefsWritable>.Context context =
EasyMock.createMock(Reducer.Context.class);
int itemIDIndex = TasteHadoopUtils.idToIndex(123L);
context.write(EasyMock.eq(new VarIntWritable(itemIDIndex)), vectorAndPrefsForFilteringMatches(123L, 456L, 789L));
EasyMock.replay(context);
new ItemFilterAsVectorAndPrefsReducer().reduce(new VarLongWritable(123L), Arrays.asList(new VarLongWritable(456L),
new VarLongWritable(789L)), context);
EasyMock.verify(context);
}
static VectorAndPrefsWritable vectorAndPrefsForFilteringMatches(final long itemID, final long... userIDs) {
EasyMock.reportMatcher(new IArgumentMatcher() {
@Override
public boolean matches(Object argument) {
if (argument instanceof VectorAndPrefsWritable) {
VectorAndPrefsWritable vectorAndPrefs = (VectorAndPrefsWritable) argument;
Vector vector = vectorAndPrefs.getVector();
if (vector.getNumNondefaultElements() != 1) {
return false;
}
if (!Double.isNaN(vector.get(TasteHadoopUtils.idToIndex(itemID)))) {
return false;
}
if (userIDs.length != vectorAndPrefs.getUserIDs().size()) {
return false;
}
for (long userID : userIDs) {
if (!vectorAndPrefs.getUserIDs().contains(userID)) {
return false;
}
}
return true;
}
return false;
}
@Override
public void appendTo(StringBuffer buffer) {}
});
return null;
}
/**
* tests {@link PartialMultiplyMapper}
*/
@Test
public void testPartialMultiplyMapper() throws Exception {
Vector similarityColumn = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
similarityColumn.set(3, 0.5);
similarityColumn.set(7, 0.8);
Mapper<VarIntWritable,VectorAndPrefsWritable,VarLongWritable,PrefAndSimilarityColumnWritable>.Context context =
EasyMock.createMock(Mapper.Context.class);
PrefAndSimilarityColumnWritable one = new PrefAndSimilarityColumnWritable();
PrefAndSimilarityColumnWritable two = new PrefAndSimilarityColumnWritable();
one.set(1.0f, similarityColumn);
two.set(3.0f, similarityColumn);
context.write(EasyMock.eq(new VarLongWritable(123L)), EasyMock.eq(one));
context.write(EasyMock.eq(new VarLongWritable(456L)), EasyMock.eq(two));
EasyMock.replay(context);
VectorAndPrefsWritable vectorAndPrefs = new VectorAndPrefsWritable(similarityColumn, Arrays.asList(123L, 456L),
Arrays.asList(1.0f, 3.0f));
new PartialMultiplyMapper().map(new VarIntWritable(1), vectorAndPrefs, context);
EasyMock.verify(context);
}
/**
* 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 AggregateAndRecommendReducer}
*/
@Test
public void testAggregateAndRecommendReducerExcludeRecommendationsBasedOnOneItem() 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)));
EasyMock.replay(context);
RandomAccessSparseVector similarityColumnOne = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
similarityColumnOne.set(1, 0.1);
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 AggregateAndRecommendReducer} with a limit on the recommendations per user
*/
@Test
public void testAggregateAndRecommendReducerLimitNumberOfRecommendations() 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)));
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", 1);
reducer.reduce(new VarLongWritable(123L), values, context);
EasyMock.verify(context);
}
/**
* verifies a {@link RecommendedItemsWritable}
*/
static RecommendedItemsWritable recommendationsMatch(final RecommendedItem... items) {
EasyMock.reportMatcher(new IArgumentMatcher() {
@Override
public boolean matches(Object argument) {
if (argument instanceof RecommendedItemsWritable) {
RecommendedItemsWritable recommendedItemsWritable = (RecommendedItemsWritable) argument;
List<RecommendedItem> expectedItems = new LinkedList<RecommendedItem>(Arrays.asList(items));
return expectedItems.equals(recommendedItemsWritable.getRecommendedItems());
}
return false;
}
@Override
public void appendTo(StringBuffer buffer) {}
});
return null;
}
/**
* small integration test that runs the full job
*
* As a tribute to http://www.slideshare.net/srowen/collaborative-filtering-at-scale,
* we recommend people food to animals in this test :)
*
* <pre>
*
* user-item-matrix
*
* burger hotdog berries icecream
* dog 5 5 2 -
* rabbit 2 - 3 5
* cow - 5 - 3
* donkey 3 - - 5
*
*
* item-item-similarity-matrix (tanimoto-coefficient of the item-vectors of the user-item-matrix)
*
* burger hotdog berries icecream
* burger - 0.25 0.66 0.5
* hotdog 0.25 - 0.33 0.25
* berries 0.66 0.33 - 0.25
* icecream 0.5 0.25 0.25 -
*
*
* Prediction(dog, icecream) = (0.5 * 5 + 0.25 * 5 + 0.25 * 2 ) / (0.5 + 0.25 + 0.25) ~ 4.3
* Prediction(rabbit, hotdog) = (0.25 * 2 + 0.33 * 3 + 0.25 * 5) / (0.25 + 0.33 + 0.25) ~ 3,3
* Prediction(cow, burger) = (0.25 * 5 + 0.5 * 3) / (0.25 + 0.5) ~ 3,7
* Prediction(cow, berries) = (0.33 * 5 + 0.25 * 3) / (0.33 + 0.25) ~ 4,1
* Prediction(donkey, hotdog) = (0.25 * 3 + 0.25 * 5) / (0.25 + 0.25) ~ 4
* Prediction(donkey, berries) = (0.66 * 3 + 0.25 * 5) / (0.66 + 0.25) ~ 3,5
*
* </pre>
*/
@Test
public void testCompleteJob() throws Exception {
File inputFile = getTestTempFile("prefs.txt");
File outputDir = getTestTempDir("output");
outputDir.delete();
File tmpDir = getTestTempDir("tmp");
writeLines(inputFile,
"1,1,5",
"1,2,5",
"1,3,2",
"2,1,2",
"2,3,3",
"2,4,5",
"3,2,5",
"3,4,3",
"4,1,3",
"4,4,5");
RecommenderJob recommenderJob = new RecommenderJob();
Configuration conf = new Configuration();
conf.set("mapred.input.dir", inputFile.getAbsolutePath());
conf.set("mapred.output.dir", outputDir.getAbsolutePath());
conf.setBoolean("mapred.output.compress", false);
recommenderJob.setConf(conf);
recommenderJob.run(new String[] { "--tempDir", tmpDir.getAbsolutePath(), "--similarityClassname",
TanimotoCoefficientSimilarity.class.getName(), "--numRecommendations", "4" });
Map<Long,List<RecommendedItem>> recommendations = readRecommendations(new File(outputDir, "part-r-00000"));
assertEquals(4, recommendations.size());
for (Entry<Long,List<RecommendedItem>> entry : recommendations.entrySet()) {
long userID = entry.getKey();
List<RecommendedItem> items = entry.getValue();
assertNotNull(items);
RecommendedItem item1 = items.get(0);
if (userID == 1L) {
assertEquals(1, items.size());
assertEquals(4L, item1.getItemID());
assertEquals(4.3, item1.getValue(), 0.05);
}
if (userID == 2L) {
assertEquals(1, items.size());
assertEquals(2L, item1.getItemID());
assertEquals(3.3, item1.getValue(), 0.05);
}
if (userID == 3L) {
assertEquals(2, items.size());
assertEquals(3L, item1.getItemID());
assertEquals(4.1, item1.getValue(), 0.05);
RecommendedItem item2 = items.get(1);
assertEquals(1L, item2.getItemID());
assertEquals(3.7, item2.getValue(), 0.05);
}
if (userID == 4L) {
assertEquals(2, items.size());
assertEquals(2L, item1.getItemID());
assertEquals(4.0, item1.getValue(), 0.05);
RecommendedItem item2 = items.get(1);
assertEquals(3L, item2.getItemID());
assertEquals(3.5, item2.getValue(), 0.05);
}
}
}
/**
* small integration test for boolean data
*/
@Test
public void testCompleteJobBoolean() throws Exception {
File inputFile = getTestTempFile("prefs.txt");
File outputDir = getTestTempDir("output");
outputDir.delete();
File tmpDir = getTestTempDir("tmp");
File usersFile = getTestTempFile("users.txt");
writeLines(usersFile, "3");
writeLines(inputFile,
"1,1",
"1,2",
"1,3",
"2,1",
"2,3",
"2,4",
"3,2",
"3,4",
"4,1",
"4,4");
RecommenderJob recommenderJob = new RecommenderJob();
Configuration conf = new Configuration();
conf.set("mapred.input.dir", inputFile.getAbsolutePath());
conf.set("mapred.output.dir", outputDir.getAbsolutePath());
conf.setBoolean("mapred.output.compress", false);
recommenderJob.setConf(conf);
recommenderJob.run(new String[] { "--tempDir", tmpDir.getAbsolutePath(), "--similarityClassname",
CooccurrenceCountSimilarity.class.getName(), "--booleanData", "true",
"--usersFile", usersFile.getAbsolutePath() });
Map<Long,List<RecommendedItem>> recommendations = readRecommendations(new File(outputDir, "part-r-00000"));
List<RecommendedItem> recommendedToCow = recommendations.get(3L);
assertEquals(2, recommendedToCow.size());
RecommendedItem item1 = recommendedToCow.get(0);
RecommendedItem item2 = recommendedToCow.get(1);
assertEquals(1L, item1.getItemID());
assertEquals(3L, item2.getItemID());
/* predicted pref must be the sum of similarities:
* item1: coocc(burger, hotdog) + coocc(burger, icecream) = 3
* item2: coocc(berries, hotdog) + coocc(berries, icecream) = 2 */
assertEquals(3, item1.getValue(), 0.05);
assertEquals(2, item2.getValue(), 0.05);
}
/**
* check whether the explicit user/item filter works
*/
@Test
public void testCompleteJobWithFiltering() throws Exception {
File inputFile = getTestTempFile("prefs.txt");
File userFile = getTestTempFile("users.txt");
File filterFile = getTestTempFile("filter.txt");
File outputDir = getTestTempDir("output");
outputDir.delete();
File tmpDir = getTestTempDir("tmp");
writeLines(inputFile,
"1,1,5",
"1,2,5",
"1,3,2",
"2,1,2",
"2,3,3",
"2,4,5",
"3,2,5",
"3,4,3",
"4,1,3",
"4,4,5");
/* only compute recommendations for the donkey */
writeLines(userFile, "4");
/* do not recommend the hotdog for the donkey */
writeLines(filterFile, "4,2");
RecommenderJob recommenderJob = new RecommenderJob();
Configuration conf = new Configuration();
conf.set("mapred.input.dir", inputFile.getAbsolutePath());
conf.set("mapred.output.dir", outputDir.getAbsolutePath());
conf.setBoolean("mapred.output.compress", false);
recommenderJob.setConf(conf);
recommenderJob.run(new String[] { "--tempDir", tmpDir.getAbsolutePath(), "--similarityClassname",
TanimotoCoefficientSimilarity.class.getName(), "--numRecommendations", "1",
"--usersFile", userFile.getAbsolutePath(), "--filterFile", filterFile.getAbsolutePath() });
Map<Long,List<RecommendedItem>> recommendations = readRecommendations(new File(outputDir, "part-r-00000"));
assertEquals(1, recommendations.size());
assertTrue(recommendations.containsKey(4L));
assertEquals(1, recommendations.get(4L).size());
/* berries should have been recommended to the donkey */
RecommendedItem recommendedItem = recommendations.get(4L).get(0);
assertEquals(3L, recommendedItem.getItemID());
assertEquals(3.5, recommendedItem.getValue(), 0.05);
}
static Map<Long,List<RecommendedItem>> readRecommendations(File file) throws IOException {
Map<Long,List<RecommendedItem>> recommendations = Maps.newHashMap();
Iterable<String> lineIterable = new FileLineIterable(file);
for (String line : lineIterable) {
String[] keyValue = line.split("\t");
long userID = Long.parseLong(keyValue[0]);
String[] tokens = keyValue[1].replaceAll("\\[", "")
.replaceAll("\\]", "").split(",");
List<RecommendedItem> items = new LinkedList<RecommendedItem>();
for (String token : tokens) {
String[] itemTokens = token.split(":");
long itemID = Long.parseLong(itemTokens[0]);
float value = Float.parseFloat(itemTokens[1]);
items.add(new GenericRecommendedItem(itemID, value));
}
recommendations.put(userID, items);
}
return recommendations;
}
}