/**
* 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.eval;
import java.util.Collection;
import java.util.concurrent.Callable;
import java.util.concurrent.atomic.AtomicInteger;
import com.google.common.collect.Lists;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.common.FullRunningAverageAndStdDev;
import org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator;
import org.apache.mahout.cf.taste.impl.common.RunningAverageAndStdDev;
import org.apache.mahout.cf.taste.impl.common.SamplingLongPrimitiveIterator;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.Recommender;
/**
* Simple helper class for running load on a Recommender.
*/
public final class LoadEvaluator {
private LoadEvaluator() { }
public static LoadStatistics runLoad(Recommender recommender) throws TasteException {
return runLoad(recommender, 10);
}
public static LoadStatistics runLoad(Recommender recommender, int howMany) throws TasteException {
DataModel dataModel = recommender.getDataModel();
int numUsers = dataModel.getNumUsers();
double sampleRate = 1000.0 / numUsers;
LongPrimitiveIterator userSampler =
SamplingLongPrimitiveIterator.maybeWrapIterator(dataModel.getUserIDs(), sampleRate);
recommender.recommend(userSampler.next(), howMany); // Warm up
Collection<Callable<Void>> callables = Lists.newArrayList();
while (userSampler.hasNext()) {
callables.add(new LoadCallable(recommender, userSampler.next()));
}
AtomicInteger noEstimateCounter = new AtomicInteger();
RunningAverageAndStdDev timing = new FullRunningAverageAndStdDev();
AbstractDifferenceRecommenderEvaluator.execute(callables, noEstimateCounter, timing);
return new LoadStatistics(timing);
}
}