@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();
}
/**
* Check that we score items but do not provide scores for items the user has previously rated.
* User 5 has rated only item 8 previously.
*/
@Test
public void testItemScorerNoRating() {
long[] items = {7, 8};
ItemItemScorer scorer = session.get(ItemItemScorer.class);
assertThat(scorer, notNullValue());
SparseVector scores = scorer.score(5, LongArrayList.wrap(items));
assertThat(scores, notNullValue());
assertThat(scores.size(), equalTo(1));
assertThat(scores.get(7), not(notANumber()));
assertThat(scores.containsKey(8), equalTo(false));
}
/**
* Check that we score items but do not provide scores for items the user has previously rated.
* User 5 has rated only item 8 previously.
*/
@Test
public void testItemScorerChannels() {
long[] items = {7, 8};
ItemItemScorer scorer = session.get(ItemItemScorer.class);
assertThat(scorer, notNullValue());
SparseVector scores = scorer.score(5, LongArrayList.wrap(items));
assertThat(scores, notNullValue());
assertThat(scores.size(), equalTo(1));
assertThat(scores.get(7), not(notANumber()));
assertThat(
scores.getChannelVector(ItemItemScorer.NEIGHBORHOOD_SIZE_SYMBOL).get(7),
closeTo(1.0, 1.0e-5));
assertThat(scores.containsKey(8), equalTo(false));
long[] items2 = {7, 8, 9};
scorer = session.get(ItemItemScorer.class);
assertThat(scorer, notNullValue());
scores = scorer.score(2, LongArrayList.wrap(items2));
assertThat(
scores.getChannelVector(ItemItemScorer.NEIGHBORHOOD_SIZE_SYMBOL).get(9),
closeTo(3.0, 1.0e-5)); // 1, 7, 8
}
@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);
}
public static void main(String[] args) throws RecommenderBuildException {
LenskitConfiguration config = configureRecommender();
logger.info("building recommender");
Recommender rec = LenskitRecommender.build(config);
if (args.length == 0) {
logger.error("No users specified; provide user IDs as command line arguments");
}
// we automatically get a useful recommender since we have a scorer
ItemRecommender irec = rec.getItemRecommender();
assert irec != null;
try {
// Generate 5 recommendations for each user
for (String user : args) {
long uid;
try {
uid = Long.parseLong(user);
} catch (NumberFormatException e) {
logger.error("cannot parse user {}", user);
continue;
}
logger.info("searching for recommendations for user {}", user);
List<ScoredId> recs = irec.recommend(uid, 5);
if (recs.isEmpty()) {
logger.warn("no recommendations for user {}, do they exist?", uid);
}
System.out.format("recommendations for user %d:\n", uid);
for (ScoredId id : recs) {
System.out.format(" %d: %.4f\n", id.getId(), id.getScore());
}
}
} catch (UnsupportedOperationException e) {
if (e.getMessage().equals("stub implementation")) {
System.out.println("Congratulations, the stub builds and runs!");
}
}
}
