Example usage for org.apache.mahout.cf.taste.impl.similarity UncenteredCosineSimilarity UncenteredCosineSimilarity

List of usage examples for org.apache.mahout.cf.taste.impl.similarity UncenteredCosineSimilarity UncenteredCosineSimilarity

  1. HOME
  2. Java
  3. org.apache.mahout
  4. org.apache.mahout.cf.taste.impl.similarity.*
  5. UncenteredCosineSimilarity
  6. UncenteredCosineSimilarity

Introduction

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

Prototype

public UncenteredCosineSimilarity(DataModel dataModel) throws TasteException

Source Link

Usage

From source file:edu.nudt.c6.datasetlinking.mahout.MyRecommenderBuilder.java

License:Apache License

@Override
public Recommender buildRecommender(DataModel dataModel) throws TasteException {
    if (recommenderType == RECOMMENDER.ITEM) {
        ItemSimilarity itemSimilarity = null;
        switch (similarityType) {
        case PEARSON:
            itemSimilarity = new PearsonCorrelationSimilarity(dataModel);
            break;
        case PEARSON_WEIGHTED:
            itemSimilarity = new PearsonCorrelationSimilarity(dataModel, Weighting.WEIGHTED);
            break;
        case COSINE:
            itemSimilarity = new UncenteredCosineSimilarity(dataModel);
            break;
        case TANIMOTO:
            itemSimilarity = new TanimotoCoefficientSimilarity(dataModel);
            break;
        case LOGLIKELIHOOD:
            itemSimilarity = new LogLikelihoodSimilarity(dataModel);
            break;
        case CITYBLOCK:
            itemSimilarity = new CityBlockSimilarity(dataModel);
            break;
        case EUCLIDEAN:
            itemSimilarity = new EuclideanDistanceSimilarity(dataModel);
            break;
        case EUCLIDEAN_WEIGHTED:
            itemSimilarity = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED);
            break;
        case DATASET_VOCABULARY_COSINE:
            try {
                itemSimilarity = new DatasetVocabularySimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();//from   w ww .  j a va2s .co  m
            }
            break;
        case DATASET_CLASS_COSINE:
            try {
                itemSimilarity = new DatasetClassSimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        case DATASET_PPROPERTY_COSINE_SUBJECTS:
            try {
                itemSimilarity = new DatasetPropertySubjectsSimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        case DATASET_PPROPERTY_COSINE_TRIPLES:
            try {
                itemSimilarity = new DatasetPropertyTriplesSimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        default:
            itemSimilarity = new EuclideanDistanceSimilarity(dataModel);
        }

        if (pref) {
            return new GenericItemBasedRecommender(dataModel, itemSimilarity);
        } else {
            return new GenericBooleanPrefItemBasedRecommender(dataModel, itemSimilarity);
        }

    } else if (recommenderType == RECOMMENDER.USER) {
        UserSimilarity userSimilarity = null;
        switch (similarityType) {
        case PEARSON:
            userSimilarity = new PearsonCorrelationSimilarity(dataModel);
            break;
        case PEARSON_WEIGHTED:
            userSimilarity = new PearsonCorrelationSimilarity(dataModel, Weighting.WEIGHTED);
            break;
        case COSINE:
            userSimilarity = new UncenteredCosineSimilarity(dataModel);
            break;
        case SPEARMAN:
            userSimilarity = new SpearmanCorrelationSimilarity(dataModel);
            break;
        case TANIMOTO:
            userSimilarity = new TanimotoCoefficientSimilarity(dataModel);
            break;
        case LOGLIKELIHOOD:
            userSimilarity = new LogLikelihoodSimilarity(dataModel);
            break;
        case CITYBLOCK:
            userSimilarity = new CityBlockSimilarity(dataModel);
            break;
        case EUCLIDEAN:
            userSimilarity = new EuclideanDistanceSimilarity(dataModel);
            break;
        case EUCLIDEAN_WEIGHTED:
            userSimilarity = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED);
            break;
        case DATASET_VOCABULARY_COSINE:
            try {
                userSimilarity = new DatasetVocabularySimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        case DATASET_CLASS_COSINE:
            try {
                userSimilarity = new DatasetClassSimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        case DATASET_PPROPERTY_COSINE_SUBJECTS:
            try {
                userSimilarity = new DatasetPropertySubjectsSimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        case DATASET_PPROPERTY_COSINE_TRIPLES:
            try {
                userSimilarity = new DatasetPropertyTriplesSimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        default:
            userSimilarity = new EuclideanDistanceSimilarity(dataModel);
        }

        UserNeighborhood userNeighborhood = null;
        switch (neighborhoodType) {
        case NEAREST:
            userNeighborhood = new NearestNUserNeighborhood(this.nearestNum, userSimilarity, dataModel);
            break;
        case THRESHOLD:
        default:
            userNeighborhood = new ThresholdUserNeighborhood(this.neighborThreshold, userSimilarity, dataModel);
        }

        if (pref) {
            return new GenericUserBasedRecommender(dataModel, userNeighborhood, userSimilarity);
        } else {
            return new GenericBooleanPrefUserBasedRecommender(dataModel, userNeighborhood, userSimilarity);
        }
    } else if (recommenderType == RECOMMENDER.SVD) {
        AbstractFactorizer factorizer = null;

        switch (SVDfactorizerType) {
        case RatingSGD:
            factorizer = new RatingSGDFactorizer(dataModel, factorNum, iterationNum);
            break;
        case ALSWR:
            factorizer = new ALSWRFactorizer(dataModel, factorNum, lambda, iterationNum);
            break;
        case SVDPlusPlus:
            factorizer = new SVDPlusPlusFactorizer(dataModel, factorNum, iterationNum);
            break;
        case ParallelSGD:
            factorizer = new ParallelSGDFactorizer(dataModel, factorNum, lambda, iterationNum);
            break;
        case MyRatingSGD:
            factorizer = new MyRatingSGDFactorizer(dataModel, factorNum, iterationNum);
            break;
        }

        return new SVDRecommender(dataModel, factorizer);

    } else if (recommenderType == RECOMMENDER.LINKDOCUMENT) {

    } else if (recommenderType == RECOMMENDER.CollaborativeRanking) {
        AbstractCRFactorizer factorizer = null;

        switch (CRFactorizerType) {
        case BasicLFM:
            try {
                factorizer = new BasicLFMFactorizer(dataModel, factorNum, iterationNum);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        case LFMTrans:
            try {
                factorizer = new LFMTransFactorizer(dataModel, factorNum, iterationNum, learningRate);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        }
        return new CollaborativeRankingRecommender(dataModel, factorizer);
    } else if (recommenderType == RECOMMENDER.Random) {
        return new RandomRecommender(dataModel);
    } else if (recommenderType == RECOMMENDER.ItemAverage) {
        return new ItemAverageRecommender(dataModel);
    } else if (recommenderType == RECOMMENDER.ItemUserAverage) {
        return new ItemUserAverageRecommender(dataModel);
    }

    return null;

}

From source file:edu.uniandes.movie.recommenders.ItemRecommender.java

public void init() throws TasteException, IOException {
    DataModel dm = new FileDataModel(new File(""));
    ItemSimilarity is = new UncenteredCosineSimilarity(dm);
    recommender = new GenericItemBasedRecommender(dm, is);
}

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  a  va 2s.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) });
}

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

License:Apache License

public static UserSimilarity userSimilarity(SIMILARITY type, DataModel m) throws TasteException {
    switch (type) {
    case PEARSON:
        return new PearsonCorrelationSimilarity(m);
    case COSINE://from w  w w  .  j  a va  2  s.c  om
        return new UncenteredCosineSimilarity(m);
    case TANIMOTO:
        return new TanimotoCoefficientSimilarity(m);
    case LOGLIKELIHOOD:
        return new LogLikelihoodSimilarity(m);
    case SPEARMAN:
        return new SpearmanCorrelationSimilarity(m);
    case CITYBLOCK:
        return new CityBlockSimilarity(m);
    case EUCLIDEAN:
    default:
        return new EuclideanDistanceSimilarity(m);
    }
}

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

License:Apache License

public static ItemSimilarity itemSimilarity(SIMILARITY type, DataModel m) throws TasteException {
    switch (type) {
    case PEARSON:
        return new PearsonCorrelationSimilarity(m);
    case COSINE://from  w ww  .  j  a  va2s  . co m
        return new UncenteredCosineSimilarity(m);
    case TANIMOTO:
        return new TanimotoCoefficientSimilarity(m);
    case LOGLIKELIHOOD:
        return new LogLikelihoodSimilarity(m);
    case CITYBLOCK:
        return new CityBlockSimilarity(m);
    case EUCLIDEAN:
    default:
        return new EuclideanDistanceSimilarity(m);
    }
}

From source file:uit.tkorg.pr.method.cf.KNNCF.java

public static void CosineRecommend(String inputFile, int k, int n, String outputFile)
        throws IOException, TasteException {
    File userPreferencesFile = new File(inputFile);
    DataModel dataModel = new FileDataModel(userPreferencesFile);

    UserSimilarity userSimilarity = new UncenteredCosineSimilarity(dataModel);
    UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(k, userSimilarity, dataModel);

    // Create a generic user based recommender with the dataModel, the userNeighborhood and the userSimilarity
    Recommender genericRecommender = new GenericUserBasedRecommender(dataModel, userNeighborhood,
            userSimilarity);//from   ww  w  . j  av  a  2 s  .co  m
    BufferedWriter bw = new BufferedWriter(new FileWriter(outputFile));

    // Recommend 5 items for each user
    for (LongPrimitiveIterator iterator = dataModel.getUserIDs(); iterator.hasNext();) {
        long userId = iterator.nextLong();

        // Generate a list of 5 recommendations for the user
        List<RecommendedItem> itemRecommendations = genericRecommender.recommend(userId, n);

        if (!itemRecommendations.isEmpty()) {
            // Display the list of recommendations
            for (RecommendedItem recommendedItem : itemRecommendations) {
                bw.write(
                        userId + "," + recommendedItem.getItemID() + "," + recommendedItem.getValue() + "\r\n");
            }
        }
    }
    bw.close();
}

From source file:uit.tkorg.pr.method.cf.KNNCF.java

/**
 * //from   w w  w  .  java 2 s  . com
 * @param inputFile
 * @param similarityScheme: 1: CoPearson, 2: Cosine.
 * @param k
 * @param authorTestSet
 * @param outputFile
 * @throws IOException
 * @throws TasteException 
 */
public static void computeCFRatingAndPutIntoModelForAuthorList(String inputFile, int similarityScheme, int k,
        HashMap<String, Author> authorTestSet, HashSet<String> paperIdsInTestSet, String outputFile)
        throws IOException, TasteException {
    DataModel dataModel = new FileDataModel(new File(inputFile));

    UserSimilarity userSimilarity = null;
    if (similarityScheme == 1) {
        userSimilarity = new PearsonCorrelationSimilarity(dataModel);
    } else if (similarityScheme == 2) {
        userSimilarity = new UncenteredCosineSimilarity(dataModel);
    }
    UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(k, userSimilarity, dataModel);

    // Create a generic user based recommender with the dataModel, the userNeighborhood and the userSimilarity
    Recommender genericRecommender = new GenericUserBasedRecommender(dataModel, userNeighborhood,
            userSimilarity);
    FileUtils.deleteQuietly(new File(outputFile));
    try (BufferedWriter bw = new BufferedWriter(new FileWriter(outputFile))) {
        int count = 0;
        System.out.println("Number of users:" + authorTestSet.size());
        for (LongPrimitiveIterator iterator = dataModel.getUserIDs(); iterator.hasNext();) {
            long userId = iterator.nextLong();
            // Generate a list of n recommendations for the user
            if (authorTestSet.containsKey(String.valueOf(userId).trim())) {
                System.out.println("Computing CF rating value for user no. " + count);
                List<RecommendedItem> recommendationList = genericRecommender.recommend(userId,
                        dataModel.getNumItems());
                if (!recommendationList.isEmpty()) {
                    // Display the list of recommendations
                    for (RecommendedItem recommendedItem : recommendationList) {
                        String authorId = String.valueOf(userId).trim();
                        String paperId = String.valueOf(recommendedItem.getItemID()).trim();
                        if (paperIdsInTestSet.contains(paperId)) {
                            authorTestSet.get(authorId).getCfRatingHM().put(paperId,
                                    Float.valueOf(recommendedItem.getValue()));
                            bw.write(userId + "," + recommendedItem.getItemID() + ","
                                    + recommendedItem.getValue() + "\r\n");
                        }
                    }
                }
                count++;
            }
        }
    }
}