/*
* 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 org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;
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.similarity.EuclideanDistanceSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
import java.io.File;
public final class LoadEvaluationRunner {
private static final int LOOPS = 10;
private LoadEvaluationRunner() {
}
public static void main(String[] args) throws Exception {
DataModel model = new FileDataModel(new File(args[0]));
int howMany = 10;
if (args.length > 1) {
howMany = Integer.parseInt(args[1]);
}
System.out.println("Run Items");
ItemSimilarity similarity = new EuclideanDistanceSimilarity(model);
Recommender recommender = new GenericItemBasedRecommender(model, similarity); // Use an item-item recommender
for (int i = 0; i < LOOPS; i++) {
LoadStatistics loadStats = LoadEvaluator.runLoad(recommender, howMany);
System.out.println(loadStats);
}
System.out.println("Run Users");
UserSimilarity userSim = new EuclideanDistanceSimilarity(model);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(10, userSim, model);
recommender = new GenericUserBasedRecommender(model, neighborhood, userSim);
for (int i = 0; i < LOOPS; i++) {
LoadStatistics loadStats = LoadEvaluator.runLoad(recommender, howMany);
System.out.println(loadStats);
}
}
}