Example usage for org.apache.mahout.cf.taste.eval IRStatistics getPrecision

Introduction

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

Prototype

double getPrecision();

Source Link

Document

<p> See <a href="http://en.wikipedia.org/wiki/Information_retrieval#Precision">Precision</a>.

Usage

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

private void calcularPrecicionRecall() {
    try {/*  w w  w.  j  a  v a 2 s. co m*/

        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.recomendador.java

public static void evaluarPrecicionRecall(final Scanner entrada, DataModel model,
        final UserSimilarity similarity) throws IOException, TasteException {
    RandomUtils.useTestSeed();//from   ww w .jav a2s .  c  o  m
    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();/*from  w w w  .  j a v  a 2 s  .  c o 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 a v  a2 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:nl.gridline.zieook.client.tools.ZieOokEvaluatorTest.java

License:Apache License

@Test
public void evaluate5() {
    // RecommenderIRStatsEvaluator
    // ItemBasedRecommenderBuilder
    // TanimotoCoefficientSimilarity

    // IRStatistics stats =
    // evaluator.evaluate(builder, myModel, null, 3,
    // RecommenderIRStatusEvaluator.CHOOSE_THRESHOLD,
    // §1.0);

    try {/*from  w ww . j a v a 2 s . com*/
        DataModel model = createDataBooleanModel(input);

        RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
        IRStatistics evaluation = evaluator.evaluate(
                new ItemBasedRecommenderBuilder(TanimotoCoefficientSimilarity.class.getCanonicalName()),
                new BooleanDataModelBuilder(), model, null, 3,
                GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 0.9);
        LOG.info("result: " + evaluation);

        writetofile("ItemBasedRecommenderBuilder,TanimotoCoefficientSimilarity,RecommenderIRStatsEvaluator-F1,"
                + evaluation.getF1Measure() + "\n");
        // getFNMeasure
        // writetofile("ItemBasedRecommenderBuilder,EuclideanDistanceSimilarity,RecommenderIRStatsEvaluator-F1,"
        // + evaluation.getFNMeasure(n) + "\n");
        writetofile(
                "ItemBasedRecommenderBuilder,TanimotoCoefficientSimilarity,RecommenderIRStatsEvaluator-FallOut,"
                        + evaluation.getFallOut() + "\n");
        writetofile(
                "ItemBasedRecommenderBuilder,TanimotoCoefficientSimilarity,RecommenderIRStatsEvaluator-precision,"
                        + evaluation.getPrecision() + "\n");
        writetofile(
                "ItemBasedRecommenderBuilder,TanimotoCoefficientSimilarity,RecommenderIRStatsEvaluator-recall,"
                        + evaluation.getRecall() + "\n");

        evaluator = new GenericRecommenderIRStatsEvaluator();
        evaluation = evaluator.evaluate(
                new UserBasedRecommenderBuilder(TanimotoCoefficientSimilarity.class.getCanonicalName()),
                new BooleanDataModelBuilder(), model, null, 3, 3, 0.9);
        LOG.info("result: " + evaluation);

        writetofile("UserBasedRecommenderBuilder,TanimotoCoefficientSimilarity,RecommenderIRStatsEvaluator-F1,"
                + evaluation.getF1Measure() + "\n");
        // getFNMeasure
        // writetofile("ItemBasedRecommenderBuilder,EuclideanDistanceSimilarity,RecommenderIRStatsEvaluator-F1,"
        // + evaluation.getFNMeasure(n) + "\n");
        writetofile(
                "UserBasedRecommenderBuilder,TanimotoCoefficientSimilarity,RecommenderIRStatsEvaluator-FallOut,"
                        + evaluation.getFallOut() + "\n");
        writetofile(
                "UserBasedRecommenderBuilder,TanimotoCoefficientSimilarity,RecommenderIRStatsEvaluator-precision,"
                        + evaluation.getPrecision() + "\n");
        writetofile(
                "UserBasedRecommenderBuilder,TanimotoCoefficientSimilarity,RecommenderIRStatsEvaluator-recall,"
                        + evaluation.getRecall() + "\n");

    } catch (TasteException e) {
        LOG.error("faild evaulate", e);
        fail();
    }

}

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

License:Apache License

@Test
public void testRandom() throws IOException, TasteException {
    RandomUtils.useTestSeed();/*from ww  w.  j a  v  a  2s  .  c o m*/

    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) });

}

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

License:Apache License

@Test
public void testPearsonCorrelationSimilarity() throws IOException, TasteException {
    RandomUtils.useTestSeed();/*from  ww  w . ja va  2s.c  om*/

    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) });

}

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

License:Apache License

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

    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) });
}

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

License:Apache License

@Test
public void testUncenteredCosineSimilarity() throws TasteException, IOException {
    RandomUtils.useTestSeed();//from   www  .j a  v a 2 s.com

    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 ww .  ja  va  2s  . 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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