Example usage for org.apache.mahout.cf.taste.eval RecommenderBuilder RecommenderBuilder

Introduction

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

Prototype

RecommenderBuilder

Source Link

Usage

From source file:com.corchado.testRecomender.evaluarPrecionRecallUI.java

private void calcularPrecicionRecall() {
    try {/*from ww w. ja  v a  2s.com*/

        RandomUtils.useTestSeed();
        RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
        RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel model) throws TasteException {
                UserNeighborhood neighborhood = new NearestNUserNeighborhood(CantVecindad, similarity, model);
                return new GenericUserBasedRecommender(model, neighborhood, similarity);
            }
        };
        IRStatistics stats = evaluator.evaluate(recommenderBuilder, null, model, null, 2,
                GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);

        labelPrecicion.setText("Precisin: " + stats.getPrecision());
        labelRecal.setText("Recall: " + stats.getRecall());

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

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From source file:com.corchado.testRecomender.evaluarRecomendadorUI.java

private void Evaluar() {
    try {//from www . j  av  a2 s .  c o m

        RandomUtils.useTestSeed();
        RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
        RecommenderBuilder builder = new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel model) throws TasteException {

                //                UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
                UserNeighborhood neighborhood = new NearestNUserNeighborhood(CantVecindad, similarity, model);

                return new GenericUserBasedRecommender(model, neighborhood, similarity);
            }
        };

        double score = evaluator.evaluate(builder, null, model, 0.7, 1.0);
        labelEvaluacion.setText("Evaluacin: " + score);
    } catch (TasteException ex) {
        Logger.getLogger(evaluarRecomendadorUI.class.getName()).log(Level.SEVERE, null, ex);
    }
}

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From source file:com.corchado.testRecomender.recomendador.java

public static void Probar(final Scanner entrada, DataModel model, final UserSimilarity similarity)
        throws IOException, TasteException {
    RandomUtils.useTestSeed();// w ww .j  a va2  s  .co  m
    RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
    RecommenderBuilder builder = new RecommenderBuilder() {
        @Override
        public Recommender buildRecommender(DataModel model) throws TasteException {

            //                UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
            UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);

            return new GenericUserBasedRecommender(model, neighborhood, similarity);
        }
    };

    double score = evaluator.evaluate(builder, null, model, 0.7, 1.0);
    System.out.println("evaluacion: " + score);
}

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From source file:com.corchado.testRecomender.recomendador.java

public static void evaluarPrecicionRecall(final Scanner entrada, DataModel model,
        final UserSimilarity similarity) throws IOException, TasteException {
    RandomUtils.useTestSeed();/*from  ww w. java2 s .  com*/
    RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
        @Override
        public Recommender buildRecommender(DataModel model) throws TasteException {
            UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);
            return new GenericUserBasedRecommender(model, neighborhood, similarity);
        }
    };
    IRStatistics stats = evaluator.evaluate(recommenderBuilder, null, model, null, 2,
            GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);

    System.out.println("Precision: " + stats.getPrecision());
    System.out.println("Recal: " + stats.getRecall());
}

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From source file:edu.uci.ics.sourcerer.ml.db.tools.RecommenderEvaluatorTest.java

License:Open Source License

public void testEvaluate() throws Exception {
    DataModel model = getDataModel();// www .j  a v  a  2 s. co  m

    RecommenderBuilder builder = new RecommenderBuilder() {
        @Override
        public Recommender buildRecommender(DataModel dataModel) throws TasteException {

            // return new SlopeOneRecommender(dataModel);

            // return new SVDRecommender(dataModel, 25, 100);

            //return new RandomRecommender(dataModel);

            return new ApiRecommender(dataModel);

            //            return new TreeClusteringRecommender(
            //                  dataModel,
            //                  new NearestNeighborClusterSimilarity(
            //                        //UserSimilarity similarity = new EuclideanDistanceSimilarity(bcModel,Weighting.WEIGHTED);
            //                        //UserSimilarity similarity = new LogLikelihoodSimilarity(bcModel);
            //                        //UserSimilarity similarity = new PearsonCorrelationSimilarity(bcModel);
            //                        new TanimotoCoefficientSimilarity(dataModel)      
            //                  ),
            //                  0.5);
        }
    };

    RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
    IRStatistics stats = evaluator.evaluate(builder, null, model, null, 5, 1.0, 1.0);

    System.out.println(stats.getPrecision() + ", " + stats.getRecall() + ", " + stats.getF1Measure());

    //      assertEquals(0.75, stats.getPrecision(), EPSILON);
    //      assertEquals(0.75, stats.getRecall(), EPSILON);
    //      assertEquals(0.75, stats.getF1Measure(), EPSILON);
}

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From source file:edu.uniandes.yelp.recommender.CFRecommender.java

public void evaluate() {
    try {/*from w  w w. j ava  2  s  . c om*/
        System.out.println("Iniciando evaluacion");
        RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
        System.out.println("Creando constructor");
        RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel model) throws TasteException {
                ItemSimilarity is = new PearsonCorrelationSimilarity(model);
                return new GenericItemBasedRecommender(model, is);
            }

        };
        System.out.println("Iniciando Evaluacion");
        IRStatistics stats = evaluator.evaluate(recommenderBuilder, null, dm, null, 2,
                GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);
        System.out.println("Precision " + stats.getPrecision());
        System.out.println("Recall " + stats.getRecall());
    } catch (TasteException ex) {
        System.out.println("Boom!!! ");
        ex.printStackTrace();
    }
}

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From source file:lsdr.user.based.recommender.intro.trivial.EvaluatorIntro.java

License:Open Source License

public Double evaluate() throws TasteException {
    RandomUtils.useTestSeed();/* w ww  .  j ava2 s . co  m*/

    RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();

    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
        @Override
        public Recommender buildRecommender(DataModel model) throws TasteException {
            UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
            UserNeighborhood neighborhood = new NearestNUserNeighborhood(AMOUNT_OF_NEIGHBORS, similarity,
                    model);

            return new GenericUserBasedRecommender(model, neighborhood, similarity);
        }
    };
    //                                                                 70% as a training data
    double score = evaluator.evaluate(recommenderBuilder, null, model, 0.7, 1.0);

    return score;
}

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From source file:lsdr.user.based.recommender.intro.trivial.IREvaluatorIntro.java

License:Open Source License

public IRStatistics evaluate() throws TasteException {
    RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
        @Override/*  w w w .jav  a  2  s.c om*/
        public Recommender buildRecommender(DataModel model) throws TasteException {
            // same as RecommenderIntro // TODO prepareRecommender?
            UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
            UserNeighborhood neighborhood = new NearestNUserNeighborhood(AMOUNT_OF_NEIGHBORS, similarity,
                    model);

            return new GenericUserBasedRecommender(model, neighborhood, similarity);
        }
    };
    IRStatistics stats = evaluator.evaluate(recommenderBuilder, null, model, null, 2,
            GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);
    return stats;
}

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From source file:net.aprendizajengrande.gitrecommender.Experiment.java

License:Open Source License

public static void main(String[] args) throws Exception {

    DataModel model = new FileDataModel(new File(args[0]));

    System.out.println("For repo: " + args[0]);
    System.out.println("User-based:");

    RecommenderEvaluator evaluator = new RMSRecommenderEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
        @Override/*from w  ww .  jav a 2s  .c om*/
        public Recommender buildRecommender(DataModel model) throws TasteException {
            UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
            UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);
            return new GenericUserBasedRecommender(model, neighborhood, similarity);
        }
    };
    // Use 90% of the data to train; test using the other 10%.
    double score = evaluator.evaluate(recommenderBuilder, null, model, 0.9, 1.0);
    System.out.println(score);

    recommenderBuilder = new RecommenderBuilder() {
        @Override
        public Recommender buildRecommender(DataModel model) throws TasteException {
            ItemSimilarity similarity = new EuclideanDistanceSimilarity(model);
            //new LogLikelihoodSimilarity(model);
            return new GenericItemBasedRecommender(model, similarity);
        }
    };
    System.out.println("Item-based:");
    score = evaluator.evaluate(recommenderBuilder, null, model, 0.9, 1.0);
    System.out.println(score);
}

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

License:Apache License

@Test
public void testSanity() throws TasteException, IOException {
    RandomUtils.useTestSeed();//from   w ww .  j  a v  a2 s.  c  o m

    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);

}

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