@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())));
}
@Before
public void createRatingSource() {
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));
dao = EventCollectionDAO.create(rs);
}
/**
* Build a rating matrix from the rating data. Each user's ratings are first normalized by
* subtracting a baseline score (usually a mean).
*
* @param userMapping The index mapping of user IDs to column numbers.
* @param itemMapping The index mapping of item IDs to row numbers.
* @return A matrix storing the <i>normalized</i> user ratings.
*/
private RealMatrix createRatingMatrix(IdIndexMapping userMapping, IdIndexMapping itemMapping) {
final int nusers = userMapping.size();
final int nitems = itemMapping.size();
// Create a matrix with users on rows and items on columns
logger.info("creating {} by {} rating matrix", nusers, nitems);
RealMatrix matrix = MatrixUtils.createRealMatrix(nusers, nitems);
// populate it with data
Cursor<UserHistory<Event>> users = userEventDAO.streamEventsByUser();
try {
for (UserHistory<Event> user : users) {
// Get the row number for this user
int u = userMapping.getIndex(user.getUserId());
MutableSparseVector ratings = Ratings.userRatingVector(user.filter(Rating.class));
MutableSparseVector baselines = MutableSparseVector.create(ratings.keySet());
baselineScorer.score(user.getUserId(), baselines);
// TODO Populate this user's row with their ratings, minus the baseline scores
for (VectorEntry entry : ratings.fast(State.SET)) {
long itemid = entry.getKey();
int i = itemMapping.getIndex(itemid);
double rating = entry.getValue();
double baseline = baselines.get(itemid);
matrix.setEntry(u, i, rating - baseline);
}
}
} finally {
users.close();
}
return matrix;
}
@SuppressWarnings("deprecation")
@Before
public void setup() 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 = 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);
}
@Override
public Rating copy(Rating r) {
return Ratings.copyBuilder(r).build();
}
