Example usage for org.apache.mahout.cf.taste.impl.eval AverageAbsoluteDifferenceRecommenderEvaluator AverageAbsoluteDifferenceRecommenderEvaluator

Introduction

In this page you can find the example usage for org.apache.mahout.cf.taste.impl.eval AverageAbsoluteDifferenceRecommenderEvaluator AverageAbsoluteDifferenceRecommenderEvaluator.

Prototype

AverageAbsoluteDifferenceRecommenderEvaluator

Source Link

Usage

From source file:nl.gridline.zieook.tasks.quality.MahoutEvaluatorTest.java

License:Apache License

@Test
public void testUncenteredCosineSimilarity() throws TasteException, IOException {
    RandomUtils.useTestSeed();/* w w  w  .j  ava2  s  .  c o m*/

    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) });
}

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From source file:nl.gridline.zieook.tasks.quality.MahoutEvaluatorTest.java

License:Apache License

@Test
public void testCityBlockSimilarity() throws TasteException, IOException {
    RandomUtils.useTestSeed();//from   w  w  w . j  a  va2s .c o  m

    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) });
}

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From source file:nl.gridline.zieook.tasks.quality.MahoutEvaluatorTest.java

License:Apache License

@Test
public void testLogLikelihoodSimilarity() throws TasteException, IOException {
    RandomUtils.useTestSeed();//w w  w.j  av a  2s  .co m

    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) });
}

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From source file:nl.gridline.zieook.tasks.quality.MahoutEvaluatorTest.java

License:Apache License

@Test
public void testEuclideanDistanceSimilarity() throws TasteException, IOException {
    RandomUtils.useTestSeed();//from w  w  w. j  a v a  2  s. c om

    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) });
}

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From source file:smartcityrecommender.Recommender.java

License:Open Source License

public static void test() {
    try {//from  www  .ja v  a 2 s.  co m
        // Generates repeatable results
        // This is only used in such examples, and unit tests, to
        // guarantee repeatable results. Dont use it in the real code
        // Without the call to RandomUtils.useTestSeed(), the result you see would vary significantly
        // due to the random selection of training data and test data
        //RandomUtils.useTestSeed();
        //DataModel model = new FileDataModel(new File("C:\\Users\\cenni\\Downloads\\dataset.csv"));
        DataModel model = rdm;
        RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
        //RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
        RecommenderBuilder builder = new MyRecommenderBuilder();
        double result = evaluator.evaluate(builder, null, model, 0.7, 1.0);
        System.out.println(result);
        /*IRStatistics stats = evaluator.evaluate(
         builder, null, model, null, 2,
         GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD,
         1.0);
         System.out.println(stats.getPrecision());
         System.out.println(stats.getRecall());*/

    } catch (TasteException ex) {
        Logger.getLogger(Recommender.class.getName()).log(Level.SEVERE, null, ex);
    }
}

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From source file:tv.icntv.recommend.algorithm.test.RecommendFactory.java

License:Apache License

public static RecommenderEvaluator buildEvaluator(EVALUATOR type) {
    switch (type) {
    case RMS:/* w ww. j a v  a  2  s .c o m*/
        return new RMSRecommenderEvaluator();
    case AVERAGE_ABSOLUTE_DIFFERENCE:
    default:
        return new AverageAbsoluteDifferenceRecommenderEvaluator();
    }
}

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