@Override public String toString() { return MoreObjects.toStringHelper(RatingMatrixEntry.class) .add("index", getIndex()) .add("user", getUserId()) .add("item", getItemId()) .add("value", getValue()) .toString(); } }
/** * Initialize the training estimator. * @param snap The getEntry snapshot. * @param baseline The baseline predictor. * @param dom The getEntry domain (for clamping). */ TrainingEstimator(RatingMatrix snap, BiasModel baseline, PreferenceDomain dom) { ratings = snap.getRatings(); domain = dom; estimates = new double[ratings.size()]; final LongCollection userIds = snap.getUserIds(); LongIterator userIter = userIds.iterator(); double global = baseline.getIntercept(); for (RatingMatrixEntry r: snap.getRatings()) { double userBias = baseline.getUserBias(r.getUserId()); double itemBias = baseline.getItemBias(r.getItemId()); estimates[r.getIndex()] = global + userBias + itemBias; } }
@Test public void testGetRatings() { List<RatingMatrixEntry> ratings = snap.getRatings(); assertEquals(20, ratings.size()); for (int i = 0; i < 20; i++) { RatingMatrixEntry entry = ratings.get(i); assertThat(entry.getIndex(), equalTo(i)); assertThat(ratingList, Matchers.<List<Rating>>anyOf(hasItem(rating(entry.getUserId(), entry.getItemId(), entry.getValue(), 1)), hasItem(rating(entry.getUserId(), entry.getItemId(), entry.getValue(), 2)))); } }
@Override public synchronized Long2DoubleMap getUserRatingVector(long userId) { // FIXME Don't make this so locky if (cache == null) { cache = new Long2ObjectOpenHashMap<>(); } Long2DoubleMap data = cache.get(userId); if (data != null) { return data; } else { Collection<RatingMatrixEntry> prefs = this.getUserRatings(userId); Long2DoubleMap map = new Long2DoubleOpenHashMap(); for (RatingMatrixEntry e: prefs) { map.put(e.getItemId(), e.getValue()); } data = new Long2DoubleSortedArrayMap(map); cache.put(userId, data); return data; } }
@Test public void testAddPreference() { int idx = bld.add(10, 39, 3.5); assertThat(idx, equalTo(0)); assertThat(bld.size(), equalTo(1)); PackedRatingData data = bld.build(); assertThat(data.size(), equalTo(1)); RatingMatrixEntry p2 = data.getEntry(0); assertThat(p2.getIndex(), equalTo(0)); assertThat(p2.getUserIndex(), equalTo(0)); assertThat(p2.getItemIndex(), equalTo(0)); assertThat(p2.getUserId(), equalTo(10L)); assertThat(p2.getItemId(), equalTo(39L)); assertThat(p2.getValue(), equalTo(3.5)); }
@Test public void testGetRatings() throws Exception { RandomDataSplitStrategyProvider splitData = new RandomDataSplitStrategyProvider(snapshot, new Random(), 0, 0.2); DataSplitStrategy splitStrategy = splitData.get(); KeyIndex userIndex = splitStrategy.getUserIndex(); KeyIndex itemIndex = splitStrategy.getItemIndex(); List<RatingMatrixEntry> validations = splitStrategy.getValidationRatings(); List<RatingMatrixEntry> trainingRatings = splitStrategy.getTrainRatings(); for (RatingMatrixEntry re : trainingRatings) { int user = re.getUserIndex(); int item = re.getItemIndex(); long userId = userIndex.getKey(user); long itemId = itemIndex.getKey(item); double rating = re.getValue(); assertThat(rating, equalTo(snapshot.getUserRatingVector(userId).get(itemId))); assertThat(rating, equalTo(data.get(itemId).get(userId))); } for (RatingMatrixEntry re : validations) { long userId = re.getUserId(); long itemId = re.getItemId(); double rating = re.getValue(); assertThat(rating, equalTo(snapshot.getUserRatingVector(userId).get(itemId))); assertThat(rating, equalTo(data.get(itemId).get(userId))); } }
int item = re.getItemIndex(); long userId = re.getUserId(); long itemId = re.getItemId(); assertThat(user, equalTo(userIndex.tryGetIndex(userId))); assertThat(item, equalTo(itemIndex.tryGetIndex(itemId)));
@Override public String toString() { return MoreObjects.toStringHelper(RatingMatrixEntry.class) .add("index", getIndex()) .add("user", getUserId()) .add("item", getItemId()) .add("value", getValue()) .toString(); } }
/** * Initialize the training estimator. * @param snap The getEntry snapshot. * @param baseline The baseline predictor. * @param dom The getEntry domain (for clamping). */ TrainingEstimator(RatingMatrix snap, BiasModel baseline, PreferenceDomain dom) { ratings = snap.getRatings(); domain = dom; estimates = new double[ratings.size()]; final LongCollection userIds = snap.getUserIds(); LongIterator userIter = userIds.iterator(); double global = baseline.getIntercept(); for (RatingMatrixEntry r: snap.getRatings()) { double userBias = baseline.getUserBias(r.getUserId()); double itemBias = baseline.getItemBias(r.getItemId()); estimates[r.getIndex()] = global + userBias + itemBias; } }
@Override public synchronized Long2DoubleMap getUserRatingVector(long userId) { // FIXME Don't make this so locky if (cache == null) { cache = new Long2ObjectOpenHashMap<>(); } Long2DoubleMap data = cache.get(userId); if (data != null) { return data; } else { Collection<RatingMatrixEntry> prefs = this.getUserRatings(userId); Long2DoubleMap map = new Long2DoubleOpenHashMap(); for (RatingMatrixEntry e: prefs) { map.put(e.getItemId(), e.getValue()); } data = new Long2DoubleSortedArrayMap(map); cache.put(userId, data); return data; } }