Java tutorial
/** * 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 */ package nl.gridline.zieook.tasks.quality; import static org.junit.Assert.assertTrue; import java.io.File; import java.io.IOException; import org.apache.mahout.cf.taste.common.TasteException; import org.apache.mahout.cf.taste.eval.IRStatistics; import org.apache.mahout.cf.taste.eval.RecommenderBuilder; import org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator; import org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluator; import org.apache.mahout.cf.taste.impl.eval.RMSRecommenderEvaluator; 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.GenericUserBasedRecommender; import org.apache.mahout.cf.taste.impl.recommender.RandomRecommender; import org.apache.mahout.cf.taste.impl.similarity.CityBlockSimilarity; import org.apache.mahout.cf.taste.impl.similarity.EuclideanDistanceSimilarity; import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity; import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity; import org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity; import org.apache.mahout.cf.taste.impl.similarity.UncenteredCosineSimilarity; 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.common.RandomUtils; import org.junit.BeforeClass; import org.junit.Test; import org.slf4j.Logger; import org.slf4j.LoggerFactory; /** * This tests the sanity of the recommender evaluator. The code is taken directly from Mahout. * <p /> * Project zieook-runner<br /> * MahoutEvaluator.java created 7 feb. 2012 * <p /> * Copyright, all rights reserved 2012 GridLine Amsterdam * @author <a href="mailto:job@gridline.nl">Job</a> * @version $Revision:$, $Date:$ */ public class MahoutEvaluatorTest { private static final Logger LOG = LoggerFactory.getLogger(MahoutEvaluatorTest.class); private static File testData; /** * @throws IOException * @throws java.lang.Exception */ @BeforeClass public static void setUpBeforeClass() throws IOException { RandomUtils.useTestSeed(); testData = new File("target/test-data.csv"); TransformData.transformData(new File("test-data-big/ratings.dat"), testData); } @Test public void testSanity() throws TasteException, IOException { RandomUtils.useTestSeed(); LOG.info("testing sanity on dummy data, result should be 1.0"); DataModel model = new FileDataModel(new File("test-data-small/intro.csv")); AverageAbsoluteDifferenceRecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator(); RecommenderBuilder builder = new RecommenderBuilder() { @Override public Recommender buildRecommender(DataModel model) throws TasteException { PearsonCorrelationSimilarity similarity = new PearsonCorrelationSimilarity(model); NearestNUserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model); return new GenericUserBasedRecommender(model, neighborhood, similarity); } }; final long start = System.currentTimeMillis(); double score = evaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("score: {} in {}ms", String.format("%.4f", score), (System.currentTimeMillis() - start)); assertTrue(1.0 == score); } @Test public void testRandom() throws IOException, TasteException { RandomUtils.useTestSeed(); LOG.info("testing Random:"); DataModel model = new FileDataModel(testData); GenericRecommenderIRStatsEvaluator evaluatorIR = new GenericRecommenderIRStatsEvaluator(); AverageAbsoluteDifferenceRecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator(); RMSRecommenderEvaluator rmsEvaluator = new RMSRecommenderEvaluator(); RecommenderBuilder builder = new RecommenderBuilder() { @Override public Recommender buildRecommender(DataModel model) throws TasteException { return new RandomRecommender(model); } }; long start = System.currentTimeMillis(); double score = evaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); score = rmsEvaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("rms score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); IRStatistics stats = evaluatorIR.evaluate(builder, null, model, null, 2, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0); LOG.info("precision: {} recall: {} in {}s", new Object[] { String.format("%.4f", stats.getPrecision()), String.format("%.4f", stats.getRecall()), ((System.currentTimeMillis() - start) / 1000) }); } @Test public void testPearsonCorrelationSimilarity() throws IOException, TasteException { RandomUtils.useTestSeed(); LOG.info("testing PearsonCorrelationSimilarity: "); DataModel model = new FileDataModel(testData); AverageAbsoluteDifferenceRecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator(); RMSRecommenderEvaluator rmsEvaluator = new RMSRecommenderEvaluator(); GenericRecommenderIRStatsEvaluator evaluatorIR = new GenericRecommenderIRStatsEvaluator(); RecommenderBuilder builder = new RecommenderBuilder() { @Override public Recommender buildRecommender(DataModel model) throws TasteException { PearsonCorrelationSimilarity similarity = new PearsonCorrelationSimilarity(model); UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model); return new GenericUserBasedRecommender(model, neighborhood, similarity); } }; long start = System.currentTimeMillis(); double score = evaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); score = rmsEvaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("rms score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); IRStatistics stats = evaluatorIR.evaluate(builder, null, model, null, 2, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0); LOG.info("precision: {} recall: {} in {}s", new Object[] { String.format("%.4f", stats.getPrecision()), String.format("%.4f", stats.getRecall()), ((System.currentTimeMillis() - start) / 1000) }); } @Test public void testTanimotoCoefficientSimilarity() throws TasteException, IOException { RandomUtils.useTestSeed(); LOG.info("testing: TanimotoCoefficientSimilarity:"); DataModel model = new FileDataModel(testData); AverageAbsoluteDifferenceRecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator(); RMSRecommenderEvaluator rmsEvaluator = new RMSRecommenderEvaluator(); GenericRecommenderIRStatsEvaluator evaluatorIR = new GenericRecommenderIRStatsEvaluator(); RecommenderBuilder builder = new RecommenderBuilder() { @Override public Recommender buildRecommender(DataModel model) throws TasteException { TanimotoCoefficientSimilarity similarity = new TanimotoCoefficientSimilarity(model); UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model); return new GenericUserBasedRecommender(model, neighborhood, similarity); } }; long start = System.currentTimeMillis(); double score = evaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("average score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); score = rmsEvaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("rms score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); IRStatistics stats = evaluatorIR.evaluate(builder, null, model, null, 2, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0); LOG.info("precision: {} recall: {} in {}s", new Object[] { String.format("%.4f", stats.getPrecision()), String.format("%.4f", stats.getRecall()), ((System.currentTimeMillis() - start) / 1000) }); } @Test public void testUncenteredCosineSimilarity() throws TasteException, IOException { RandomUtils.useTestSeed(); LOG.info("testing: UncenteredCosineSimilarity:"); DataModel model = new FileDataModel(testData); AverageAbsoluteDifferenceRecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator(); RMSRecommenderEvaluator rmsEvaluator = new RMSRecommenderEvaluator(); GenericRecommenderIRStatsEvaluator evaluatorIR = new GenericRecommenderIRStatsEvaluator(); RecommenderBuilder builder = new RecommenderBuilder() { @Override public Recommender buildRecommender(DataModel model) throws TasteException { UncenteredCosineSimilarity similarity = new UncenteredCosineSimilarity(model); UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model); return new GenericUserBasedRecommender(model, neighborhood, similarity); } }; long start = System.currentTimeMillis(); double score = evaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("average score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); score = rmsEvaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("rms score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); IRStatistics stats = evaluatorIR.evaluate(builder, null, model, null, 2, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0); LOG.info("precision: {} recall: {} in {}s", new Object[] { String.format("%.4f", stats.getPrecision()), String.format("%.4f", stats.getRecall()), ((System.currentTimeMillis() - start) / 1000) }); } @Test public void testCityBlockSimilarity() throws TasteException, IOException { RandomUtils.useTestSeed(); LOG.info("testing CityBlockSimilarity:"); DataModel model = new FileDataModel(testData); AverageAbsoluteDifferenceRecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator(); RMSRecommenderEvaluator rmsEvaluator = new RMSRecommenderEvaluator(); GenericRecommenderIRStatsEvaluator evaluatorIR = new GenericRecommenderIRStatsEvaluator(); RecommenderBuilder builder = new RecommenderBuilder() { @Override public Recommender buildRecommender(DataModel model) throws TasteException { CityBlockSimilarity similarity = new CityBlockSimilarity(model); UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model); return new GenericUserBasedRecommender(model, neighborhood, similarity); } }; long start = System.currentTimeMillis(); double score = evaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("average score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); score = rmsEvaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("rms score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); IRStatistics stats = evaluatorIR.evaluate(builder, null, model, null, 2, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0); LOG.info("precision: {} recall: {} in {}s", new Object[] { String.format("%.4f", stats.getPrecision()), String.format("%.4f", stats.getRecall()), ((System.currentTimeMillis() - start) / 1000) }); } @Test public void testLogLikelihoodSimilarity() throws TasteException, IOException { RandomUtils.useTestSeed(); LOG.info("testing LogLikelihoodSimilarity: "); DataModel model = new FileDataModel(testData); AverageAbsoluteDifferenceRecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator(); RMSRecommenderEvaluator rmsEvaluator = new RMSRecommenderEvaluator(); GenericRecommenderIRStatsEvaluator evaluatorIR = new GenericRecommenderIRStatsEvaluator(); RecommenderBuilder builder = new RecommenderBuilder() { @Override public Recommender buildRecommender(DataModel model) throws TasteException { LogLikelihoodSimilarity similarity = new LogLikelihoodSimilarity(model); UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model); return new GenericUserBasedRecommender(model, neighborhood, similarity); } }; long start = System.currentTimeMillis(); double score = evaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("average score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); score = rmsEvaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("rms score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); IRStatistics stats = evaluatorIR.evaluate(builder, null, model, null, 2, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0); LOG.info("precision: {} recall: {} in {}s", new Object[] { String.format("%.4f", stats.getPrecision()), String.format("%.4f", stats.getRecall()), ((System.currentTimeMillis() - start) / 1000) }); } @Test public void testEuclideanDistanceSimilarity() throws TasteException, IOException { RandomUtils.useTestSeed(); LOG.info("testing EuclideanDistanceSimilarity:"); DataModel model = new FileDataModel(testData); AverageAbsoluteDifferenceRecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator(); RMSRecommenderEvaluator rmsEvaluator = new RMSRecommenderEvaluator(); GenericRecommenderIRStatsEvaluator evaluatorIR = new GenericRecommenderIRStatsEvaluator(); RecommenderBuilder builder = new RecommenderBuilder() { @Override public Recommender buildRecommender(DataModel model) throws TasteException { EuclideanDistanceSimilarity similarity = new EuclideanDistanceSimilarity(model); UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model); return new GenericUserBasedRecommender(model, neighborhood, similarity); } }; long start = System.currentTimeMillis(); double score = evaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("average score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); score = rmsEvaluator.evaluate(builder, null, model, 0.7, 1.0); LOG.info("rms score: {} in {}s", String.format("%.4f", score), ((System.currentTimeMillis() - start) / 1000)); start = System.currentTimeMillis(); IRStatistics stats = evaluatorIR.evaluate(builder, null, model, null, 2, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0); LOG.info("precision: {} recall: {} in {}s", new Object[] { String.format("%.4f", stats.getPrecision()), String.format("%.4f", stats.getRecall()), ((System.currentTimeMillis() - start) / 1000) }); } @Test public void testZieook() { // now we should replace the recommender with a ZieOok recommender // and the datamodel with the zieook database // and run the test... // ... // implementing this is the annoying part. } }