Example usage for org.apache.mahout.cf.taste.impl.common FastByIDMap entrySet

Introduction

In this page you can find the example usage for org.apache.mahout.cf.taste.impl.common FastByIDMap entrySet.

Prototype

public Set<Map.Entry<Long, V>> entrySet() 

Source Link

Usage

From source file:com.anjuke.romar.mahout.persistence.FilePreferenceSource.java

License:Apache License

@Override
public void compact() {
    // ????/*from w  w  w. ja  va  2s  .c o m*/
    final long version = getCurrentVersion() - 1;
    if (version < 0) {
        return;
    }
    File latestSnapshotFile = getLatestSnapshotFile();
    if (latestSnapshotFile != null && getSnapshotFileVersion(latestSnapshotFile) == version) {
        return;
    }

    synchronized (_snapshotWriterLock) {
        PrintWriter snapshotWriter = createWriter(getSnapshotFile(version));
        try {
            final List<File> logFileList = getLogFileListUntilVersion(version);
            List<File> fileToIt = new ArrayList<File>(logFileList);
            if (latestSnapshotFile != null) {
                fileToIt.add(0, latestSnapshotFile);
            }
            PreferenceIterator it = new LogFileIterator(fileToIt);
            FastByIDMap<PreferenceArray> data = new FastByIDMap<PreferenceArray>();
            while (it.hasNext()) {
                Preference pref = it.next();
                if (it.getType() == PreferenceType.ADD) {
                    Util.applyAdd(data, pref);
                } else if (it.getType() == PreferenceType.DELETE) {
                    Util.applyRemove(data, pref);
                }
            }

            for (Entry<Long, PreferenceArray> entry : data.entrySet()) {
                PreferenceArray array = entry.getValue();
                for (int i = 0, length = array.length(); i < length; i++) {
                    long userID = array.getUserID(i);
                    long itemID = array.getItemID(i);
                    float value = array.getValue(i);
                    snapshotWriter.print(userID);
                    snapshotWriter.print(',');
                    snapshotWriter.print(itemID);
                    snapshotWriter.print(',');
                    snapshotWriter.println(value);
                }
            }

            snapshotWriter.flush();
            snapshotWriter.close();
            removeFile();
        } finally {
            snapshotWriter.close();
        }
    }
}

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From source file:com.anjuke.romar.train.LocalFileTrainer.java

License:Apache License

public static void main(String[] args) {
    RomarConfig config = RomarConfig.getInstance();

    LOG.info("init dataModel");
    DataModel dataModel = PersistenceDataModelFactory.createDataModel(config);

    MahoutServiceFactory serviceFactory = config.getMahoutServiceFactory();
    LOG.info("init in memory similarity");
    ReadableSimilarity similarity = serviceFactory.createReadableSimilarity(dataModel);
    FastByIDMap<FastByIDMap<Double>> similarityDataMap = similarity.getSimilarityMaps();

    SimilarityFileWriter writer = null;//from   w ww .j ava2  s.c om
    LOG.info("start write to " + config.getSimilarityFile());
    try {
        writer = SimilarityFileWriterFactory.createFileWriter(config);
        for (Map.Entry<Long, FastByIDMap<Double>> idValueEntry : similarityDataMap.entrySet()) {
            long id1 = idValueEntry.getKey();

            for (Map.Entry<Long, Double> entry : idValueEntry.getValue().entrySet()) {
                long id2 = entry.getKey();
                double value = entry.getValue();
                writer.write(id1, id2, value);
            }
        }
    } catch (IOException e) {
        LOG.error(e.getMessage(), e);
    } finally {
        Closeables.closeQuietly(writer);
    }
    LOG.info("train finish");
}

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From source file:lib.eval.AbstractRecommenderEvaluator.java

License:Apache License

private double getEvaluation(FastByIDMap<PreferenceArray> testUserPrefs, Recommender recommender)
        throws TasteException {
    reset();// ww  w . j  a  v  a2  s .  co  m
    Collection<Callable<Void>> estimateCallables = new ArrayList<Callable<Void>>();
    AtomicInteger noEstimateCounter = new AtomicInteger();
    for (Map.Entry<Long, PreferenceArray> entry : testUserPrefs.entrySet()) {
        estimateCallables.add(new PreferenceEstimateCallable(recommender, entry.getKey(), entry.getValue(),
                noEstimateCounter));
    }
    log.info("Beginning evaluation of {} users", estimateCallables.size());
    execute(estimateCallables, noEstimateCounter);
    //scale evaluation result so it's a percentage of rating-spread
    double rawResult = computeFinalEvaluation();
    if (Double.isNaN(getMaxPreference()) || Double.isNaN(getMinPreference())) {
        return rawResult;
    } else {
        double scaling = getMaxPreference() - getMinPreference();
        double result = rawResult / scaling;
        return result;
    }

}

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From source file:net.ufida.info.mahout.common.MemoryDiffStorage.java

License:Apache License

private void pruneInconsequentialDiffs() {
    // Go back and prune inconsequential diffs. "Inconsequential" means, here, only represented by one
    // data point, so possibly unreliable
    Iterator<Map.Entry<Long, FastByIDMap<RunningAverage>>> it1 = averageDiffs.entrySet().iterator();
    while (it1.hasNext()) {
        FastByIDMap<RunningAverage> map = it1.next().getValue();
        Iterator<Map.Entry<Long, RunningAverage>> it2 = map.entrySet().iterator();
        while (it2.hasNext()) {
            RunningAverage average = it2.next().getValue();
            if (average.getCount() <= 1) {
                it2.remove();/*from w w  w  .j a  v a  2  s. c  o  m*/
            }
        }
        if (map.isEmpty()) {
            it1.remove();
        } else {
            map.rehash();
        }
    }
    averageDiffs.rehash();
}

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From source file:recsys.evaluator.TopKRecommenderEvaluator.java

License:Apache License

private Double getEvaluation(FastByIDMap<PreferenceArray> testPrefs, Recommender recommender)
        throws TasteException {
    reset();// ww  w. j  ava2 s .c om
    Collection<Callable<Void>> estimateCallables = Lists.newArrayList();
    noEstimateCounter = new AtomicInteger();
    estimateCounter = new AtomicInteger();
    for (Map.Entry<Integer, PreferenceArray> entry : testPrefs.entrySet()) {
        estimateCallables.add(new PreferenceEstimateCallable(recommender, entry.getKey(), entry.getValue(),
                noEstimateCounter, estimateCounter));
    }
    log.info("Beginning evaluation of {} users", estimateCallables.size());
    RunningAverageAndStdDev timing = new FullRunningAverageAndStdDev();
    execute(estimateCallables, noEstimateCounter, estimateCounter, timing);
    return computeFinalEvaluation();
}

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