@Test
  public void testSnapshot() throws RecommenderBuildException {
    List<Rating> rs = new ArrayList<Rating>();
    rs.add(Ratings.make(1, 5, 2));
    rs.add(Ratings.make(1, 7, 4));
    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));
    assertThat(rec.getItemRecommender(), instanceOf(TopNItemRecommender.class));
    RatingPredictor pred = rec.getRatingPredictor();
    assertThat(pred, instanceOf(SimpleRatingPredictor.class));

    Recommender rec2 = engine.createRecommender();
    assertThat(rec2.getItemScorer(), not(sameInstance(rec.getItemScorer())));
  }
 @SuppressWarnings("deprecation")
 @Before
 public void setup() throws RecommenderBuildException {
   List<Rating> rs = new ArrayList<Rating>();
   rs.add(Rating.create(1, 6, 4));
   rs.add(Rating.create(2, 6, 2));
   rs.add(Rating.create(1, 7, 3));
   rs.add(Rating.create(2, 7, 2));
   rs.add(Rating.create(3, 7, 5));
   rs.add(Rating.create(4, 7, 2));
   rs.add(Rating.create(1, 8, 3));
   rs.add(Rating.create(2, 8, 4));
   rs.add(Rating.create(3, 8, 3));
   rs.add(Rating.create(4, 8, 2));
   rs.add(Rating.create(5, 8, 3));
   rs.add(Rating.create(6, 8, 2));
   rs.add(Rating.create(1, 9, 3));
   rs.add(Rating.create(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();
 }
Exemple #3
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  @Override
  @SuppressWarnings({"rawtypes", "unchecked"})
  public void execute() throws IOException, RecommenderBuildException {
    LenskitRecommenderEngine engine = loadEngine();

    long user = options.getLong("user");
    List<Long> items = options.get("items");

    LenskitRecommender rec = engine.createRecommender();
    RatingPredictor pred = rec.getRatingPredictor();
    if (pred == null) {
      logger.error("recommender has no rating predictor");
      throw new UnsupportedOperationException("no rating predictor");
    }

    logger.info("predicting {} items", items.size());
    Symbol pchan = getPrintChannel();
    Stopwatch timer = Stopwatch.createStarted();
    SparseVector preds = pred.predict(user, items);
    Long2ObjectMap channel = null;
    if (pchan != null) {
      for (TypedSymbol sym : preds.getChannelSymbols()) {
        if (sym.getRawSymbol().equals(pchan)) {
          channel = preds.getChannel(sym);
        }
      }
    }
    for (VectorEntry e : preds) {
      System.out.format("  %d: %.3f", e.getKey(), e.getValue());
      if (channel != null) {
        System.out.format(" (%s)", channel.get(e.getKey()));
      }
      System.out.println();
    }
    timer.stop();
    logger.info("predicted for {} items in {}", items.size(), timer);
  }