Package org.grouplens.lenskit.core

Examples of org.grouplens.lenskit.core.LenskitConfiguration

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        rs.add(Ratings.make(8, 4, 5));
        rs.add(Ratings.make(8, 5, 4));

        EventDAO dao = new EventCollectionDAO(rs);

        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(dao);
        config.bind(ItemScorer.class).to(UserUserItemScorer.class);
        config.bind(NeighborFinder.class).to(LiveNeighborFinder.class);

        engine = LenskitRecommenderEngine.build(config);
    }
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        rs.add(Ratings.make(8, 4, 5));
        rs.add(Ratings.make(8, 5, 4));

        EventDAO dao = EventCollectionDAO.create(rs);

        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(dao);
        config.bind(ItemScorer.class).to(UserUserItemScorer.class);
        config.bind(NeighborFinder.class).to(SnapshotNeighborFinder.class);

        LenskitRecommenderEngine engine = LenskitRecommenderEngine.build(config);
        Recommender rec = engine.createRecommender();
        assertThat(rec.getItemScorer(),
                   instanceOf(UserUserItemScorer.class));
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                                   ComponentCache cache) throws RecommenderConfigurationException {
        MergePool<Component,Dependency> pool = MergePool.create();

        for (AlgorithmInstance algo: algorithms) {
            logger.debug("building graph for algorithm {}", algo);
            LenskitConfiguration dataConfig = new LenskitConfiguration();
            ExecutionInfo info = ExecutionInfo.newBuilder()
                                              .setAlgorithm(algo)
                                              .setDataSet(dataset)
                                              .build();
            dataConfig.addComponent(info);
            dataset.configure(dataConfig);
            // Build the graph
            DAGNode<Component, Dependency> graph = algo.buildRecommenderGraph(dataConfig);

            if (!separateAlgorithms) {
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        this("Unnamed Algorithm");
    }

    public AlgorithmInstanceBuilder(String name) {
        this.name = name;
        config = new LenskitConfiguration();
    }
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        rs.add(Ratings.make(5, 8, 3));
        rs.add(Ratings.make(6, 8, 2));
        rs.add(Ratings.make(1, 9, 3));
        rs.add(Ratings.make(3, 9, 4));

        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(EventCollectionDAO.create(rs));
        config.bind(ItemScorer.class).to(SlopeOneItemScorer.class);
        config.bind(PreferenceDomain.class).to(new PreferenceDomainBuilder(1, 5)
                                                       .setPrecision(1)
                                                       .build());
        ItemScorer predictor = LenskitRecommenderEngine.build(config)
                                                       .createRecommender()
                                                       .getItemScorer();
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        rs.add(Ratings.make(2, 6, 3));
        rs.add(Ratings.make(1, 7, 4));
        rs.add(Ratings.make(2, 7, 4));
        rs.add(Ratings.make(3, 7, 1.5));

        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(EventCollectionDAO.create(rs));
        config.bind(ItemScorer.class).to(SlopeOneItemScorer.class);
        config.bind(PreferenceDomain.class).to(new PreferenceDomainBuilder(1, 5)
                                                       .setPrecision(1)
                                                       .build());
        ItemScorer predictor = LenskitRecommenderEngine.build(config)
                                                       .createRecommender()
                                                       .getItemScorer();
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        rs.add(Ratings.make(8, 4, 5));
        rs.add(Ratings.make(8, 5, 4));

        EventDAO dao = new EventCollectionDAO(rs);

        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(dao);
        config.bind(ItemScorer.class).to(SlopeOneItemScorer.class);
        config.bind(PreferenceDomain.class).to(new PreferenceDomain(1, 5));
        // factory.setComponent(UserVectorNormalizer.class, IdentityVectorNormalizer.class);
        config.bind(BaselineScorer.class, ItemScorer.class)
              .to(UserMeanItemScorer.class);
        config.bind(UserMeanBaseline.class, ItemScorer.class)
              .to(ItemMeanRatingItemScorer.class);
        engine = LenskitRecommenderEngine.build(config);
    }
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        return options.get("output_file");
    }

    @Override
    public void execute() throws IOException, RecommenderBuildException {
        LenskitConfiguration dataConfig = input.getConfiguration();
        LenskitRecommenderEngineBuilder builder = LenskitRecommenderEngine.newBuilder();
        for (LenskitConfiguration config: environment.loadConfigurations(getConfigFiles())) {
            builder.addConfiguration(config);
        }
        builder.addConfiguration(dataConfig, ModelDisposition.EXCLUDED);
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        rs.add(Ratings.make(5, 8, 3));
        rs.add(Ratings.make(6, 8, 2));
        rs.add(Ratings.make(1, 9, 3));
        rs.add(Ratings.make(3, 9, 4));
        EventCollectionDAO dao = new EventCollectionDAO(rs);
        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(dao);
        config.bind(ItemScorer.class).to(ItemItemScorer.class);
        // this is the default
        config.bind(UserVectorNormalizer.class)
              .to(DefaultUserVectorNormalizer.class);
        config.bind(VectorNormalizer.class)
              .to(IdentityVectorNormalizer.class);
        LenskitRecommenderEngine engine = LenskitRecommenderEngine.build(config);
        session = engine.createRecommender();
        recommender = session.getItemRecommender();
    }
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            LenskitRecommender rec;
            StopWatch timer = new StopWatch();
            timer.start();
            try {
                logger.info("{}: building recommender {}", getName(), algorithm.getName());
                LenskitConfiguration config = new LenskitConfiguration();
                inputData.configure(config);
                rec = algorithm.buildRecommender(config);
            } catch (RecommenderBuildException e) {
                throw new TaskExecutionException(getName() + ": error building recommender", e);
            }
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Related Classes of org.grouplens.lenskit.core.LenskitConfiguration

  • org.grouplens.grapht.BindingFunctionBuilder
  • org.grouplens.lenskit.cli.InputData$PackedInput
  • org.grouplens.lenskit.cli.InputData$TextInput
  • org.grouplens.lenskit.cli.TrainModel
  • org.grouplens.lenskit.config.ConfigHelpers
  • org.grouplens.lenskit.config.LenskitConfigDSL
  • org.grouplens.lenskit.eval.algorithm.AlgorithmInstanceBuilder
  • org.grouplens.lenskit.eval.graph.DumpGraphTask
  • org.grouplens.lenskit.eval.metrics.topn.ItemSelectorsTest
  • org.grouplens.lenskit.eval.TrainModelTask
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