Example usage for org.apache.mahout.cf.taste.impl.recommender.svd FilePersistenceStrategy FilePersistenceStrategy

Introduction

In this page you can find the example usage for org.apache.mahout.cf.taste.impl.recommender.svd FilePersistenceStrategy FilePersistenceStrategy.

Prototype

public FilePersistenceStrategy(File file) 

Source Link

Usage

From source file:org.plista.kornakapi.core.config.FactorizationbasedRecommenderConfig.java

License:Apache License

@Override
public KornakapiRecommender buildRecommenderFromConfig(Configuration conf, DataModel persistentData)
        throws IOException, TasteException {
    String name = this.getName();

    File modelFile = new File(conf.getModelDirectory(), name + ".model");

    PersistenceStrategy persistence = new FilePersistenceStrategy(modelFile);

    if (!modelFile.exists()) {
        createEmptyFactorization(persistence);
    }//from ww  w  . j  av  a  2s .  c  om

    CandidateItemsStrategy allUnknownItemsStrategy = new CachingAllUnknownItemsCandidateItemsStrategy(
            persistentData);

    FoldingFactorizationBasedRecommender svdRecommender = new FoldingFactorizationBasedRecommender(
            persistentData, allUnknownItemsStrategy, persistence);

    return svdRecommender;
}

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From source file:org.plista.kornakapi.core.training.FactorizationbasedInMemoryTrainer.java

License:Apache License

@Override
protected void doTrain(File targetFile, DataModel inmemoryData, int numProcessors) throws IOException {
    try {//  w  w  w  . j a  v  a  2 s  .com
        if (inmemoryData.getNumItems() >= 5 && inmemoryData.getNumUsers() >= 10) {//preventing matrix singularity
            ALSWRFactorizer factorizer = new ALSWRFactorizer(inmemoryData, conf.getNumberOfFeatures(),
                    conf.getLambda(), conf.getNumberOfIterations(), conf.isUsesImplicitFeedback(),
                    conf.getAlpha(), numProcessors);

            long start = System.currentTimeMillis();
            Factorization factorization = factorizer.factorize();
            long estimateDuration = System.currentTimeMillis() - start;

            if (log.isInfoEnabled()) {
                log.info("Model trained in {} ms", estimateDuration);
            }

            new FilePersistenceStrategy(targetFile).maybePersist(factorization);
        }
    } catch (Exception e) {
        throw new IOException(e);
    }
}

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From source file:org.plista.kornakapi.web.servlets.BaseServlet.java

License:Apache License

protected void createRecommenderForLabel(String label) throws IOException, TasteException {
    Configuration conf = getConfiguration();
    List<FactorizationbasedRecommenderConfig> factorizationbasedConfs = conf
            .getFactorizationbasedRecommenders();
    FactorizationbasedRecommenderConfig factorizationbasedConf = factorizationbasedConfs.get(0);

    String name = factorizationbasedConf.getName() + "_" + label;
    if (conf.getMaxPersistence()) {
        storages().put(label, new CandidateCacheStorageDecorator(
                new MySqlMaxPersistentStorage(conf.getStorageConfiguration(), label, getDataSource())));
    } else {//from w w w. j  av  a 2 s  .  c  o  m
        storages().put(label, new CandidateCacheStorageDecorator(
                new MySqlStorage(conf.getStorageConfiguration(), label, getDataSource())));

    }

    File modelFile = new File(conf.getModelDirectory(), name + ".model");

    PersistenceStrategy persistence = new FilePersistenceStrategy(modelFile);

    if (!modelFile.exists()) {
        createEmptyFactorization(persistence);
    }

    DataModel persistenData = storages().get(label).recommenderData();

    CandidateItemsStrategy allUnknownItemsStrategy = new CachingAllUnknownItemsCandidateItemsStrategy(
            persistenData);

    FoldingFactorizationBasedRecommender svdRecommender = new FoldingFactorizationBasedRecommender(
            persistenData, allUnknownItemsStrategy, persistence,
            conf.getFactorizationbasedRecommenders().get(0).getNumberOfThreadsForEstimation());

    setRecommender(name, svdRecommender);
    setTrainer(name, new FactorizationbasedInMemoryTrainer(factorizationbasedConf));

    String cronExpression = factorizationbasedConf.getRetrainCronExpression();
    if (cronExpression == null) {
        scheduler().addRecommenderTrainingJob(name);
    } else {
        scheduler().addRecommenderTrainingJobWithCronSchedule(name, cronExpression);
        try {
            scheduler().immediatelyTrainRecommender(name);
        } catch (SchedulerException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
    }

    if (factorizationbasedConf
            .getRetrainAfterPreferenceChanges() != RecommenderConfig.DONT_RETRAIN_ON_PREFERENCE_CHANGES) {
        ((DelegatingPreferenceChangeListenerForLabel) preferenceChangeListener())
                .addDelegate(new InMemoryPreferenceChangeListener(scheduler(), name,
                        factorizationbasedConf.getRetrainAfterPreferenceChanges()), label);
    }

    log.info("Added FactorizationBasedRecommender [{}] using [{}] features and [{}] iterations for label: {}",
            new Object[] { name, factorizationbasedConf.getNumberOfFeatures(),
                    factorizationbasedConf.getNumberOfIterations(), label });
}

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