/**
* 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.impl;
import junit.textui.TestRunner;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
import org.apache.mahout.cf.taste.impl.common.RandomUtils;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.impl.model.GenericDataModel;
import org.apache.mahout.cf.taste.impl.model.GenericItem;
import org.apache.mahout.cf.taste.impl.model.GenericPreference;
import org.apache.mahout.cf.taste.impl.model.GenericUser;
import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;
import org.apache.mahout.cf.taste.impl.recommender.CachingRecommender;
import org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.recommender.slopeone.SlopeOneRecommender;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.Item;
import org.apache.mahout.cf.taste.model.Preference;
import org.apache.mahout.cf.taste.model.User;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
import java.util.Random;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;
/**
* <p>Generates load on the whole implementation, for profiling purposes mostly.</p>
*/
public final class LoadTest extends TasteTestCase {
private static final Logger log = LoggerFactory.getLogger(LoadTest.class);
private static final int NUM_USERS = 1600;
private static final int NUM_ITEMS = 800;
private static final int NUM_PREFS = 20;
private static final int NUM_THREADS = 4;
private Random random;
@Override
public void setUp() throws Exception {
super.setUp();
random = RandomUtils.getRandom();
}
public void testSlopeOneLoad() throws Exception {
DataModel model = createModel();
Recommender recommender = new CachingRecommender(new SlopeOneRecommender(model));
doTestLoad(recommender, 60);
}
public void testItemLoad() throws Exception {
DataModel model = createModel();
ItemSimilarity itemSimilarity = new PearsonCorrelationSimilarity(model);
Recommender recommender = new CachingRecommender(new GenericItemBasedRecommender(model, itemSimilarity));
doTestLoad(recommender, 240);
}
public void testUserLoad() throws Exception {
DataModel model = createModel();
UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(10, userSimilarity, model);
Recommender recommender =
new CachingRecommender(new GenericUserBasedRecommender(model, neighborhood, userSimilarity));
doTestLoad(recommender, 40);
}
private DataModel createModel() {
List<Item> items = new ArrayList<Item>(NUM_ITEMS);
for (int i = 0; i < NUM_ITEMS; i++) {
items.add(new GenericItem<String>(String.valueOf(i)));
}
List<User> users = new ArrayList<User>(NUM_USERS);
for (int i = 0; i < NUM_USERS; i++) {
int numPrefs = random.nextInt(NUM_PREFS) + 1;
List<Preference> prefs = new ArrayList<Preference>(numPrefs);
for (int j = 0; j < numPrefs; j++) {
prefs.add(new GenericPreference(null, items.get(random.nextInt(NUM_ITEMS)), random.nextDouble()));
}
GenericUser<String> user = new GenericUser<String>(String.valueOf(i), prefs);
users.add(user);
}
return new GenericDataModel(users);
}
private void doTestLoad(Recommender recommender, int allowedTimeSec)
throws InterruptedException, ExecutionException {
ExecutorService executor = Executors.newFixedThreadPool(NUM_THREADS);
Collection<Future<?>> futures = new ArrayList<Future<?>>(NUM_THREADS);
Callable<?> loadTask = new LoadWorker(recommender);
long start = System.currentTimeMillis();
for (int i = 0; i < NUM_THREADS; i++) {
futures.add(executor.submit(loadTask));
}
for (Future<?> future : futures) {
future.get();
}
long end = System.currentTimeMillis();
long timeMS = end - start;
log.info("Load test completed in {}ms", timeMS);
assertTrue(timeMS < 1000L * (long) allowedTimeSec);
}
private final class LoadWorker implements Callable<Object> {
private final Recommender recommender;
private LoadWorker(Recommender recommender) {
this.recommender = recommender;
}
@Override
public Object call() throws TasteException {
for (int i = 0; i < NUM_USERS / 2; i++) {
recommender.recommend(String.valueOf(random.nextInt(NUM_USERS)), 10);
}
recommender.refresh(null);
for (int i = NUM_USERS / 2; i < NUM_USERS; i++) {
recommender.recommend(String.valueOf(random.nextInt(NUM_USERS)), 10);
}
return null;
}
}
public static void main(String... args) {
TestRunner.run(LoadTest.class);
}
}