Package org.grouplens.lenskit.vectors

Examples of org.grouplens.lenskit.vectors.MutableSparseVector

157
158
159
160
161
162
163
164
165
166
167
168
169
        }
        @Override
        protected Neighbor computeNext() {
            while (neighborIter.hasNext()) {
                final long neighbor = neighborIter.nextLong();
                MutableSparseVector nbrRatings = getUserRatingVector(neighbor);
                if (nbrRatings != null) {
                    ImmutableSparseVector rawRatings = nbrRatings.immutable();
                    normalizer.normalize(neighbor, rawRatings, nbrRatings);
                    final double sim = similarity.similarity(user, userVector, neighbor, nbrRatings);
                    if (acceptSimilarity(sim)) {
                        // we have found a neighbor
                        return new Neighbor(neighbor, rawRatings, sim);
View Full Code Here
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
                           MutableSparseVector scores,
                           NeighborhoodScorer scorer) {
        Predicate<ScoredId> usable = new VectorKeyPredicate(userData);

        // Create a channel for recording the neighborhoodsize
        MutableSparseVector sizeChannel = scores.getOrAddChannelVector(ItemItemScorer.NEIGHBORHOOD_SIZE_SYMBOL);
        sizeChannel.fill(0);
        // for each item, compute its prediction
        for (VectorEntry e : scores.view(VectorEntry.State.EITHER)) {
            final long item = e.getKey();

            // find all potential neighbors
            FluentIterable<ScoredId> nbrIter = FluentIterable.from(model.getNeighbors(item))
                                                             .filter(usable);
            if (neighborhoodSize > 0) {
                nbrIter = nbrIter.limit(neighborhoodSize);
            }
            List<ScoredId> neighbors = nbrIter.toList();

            // compute score & place in vector
            ScoredId score = null;

            if (neighbors.size() >= minNeighbors) {
                score = scorer.score(item, neighbors, userData);
            }

            if (score != null) {
                scores.set(e, score.getScore());
                // FIXME Scorers should not need to do this.
                for (TypedSymbol sym: score.getChannelSymbols()) {
                    scores.getOrAddChannel(sym).put(e.getKey(), score.getChannelValue(sym));
                }
            }

            sizeChannel.set(e, neighbors.size());
        }
    }
View Full Code Here
55
56
57
58
59
60
61
62
63
64
    @Override
    public void globalScore(@Nonnull Collection<Long> queryItems,
                            @Nonnull MutableSparseVector output) {
        // create the unary rating for the items
        LongSortedSet qItems = LongUtils.packedSet(queryItems);
        MutableSparseVector basket = MutableSparseVector.create(qItems, 1.0);

        output.clear();
        algorithm.scoreItems(model, basket, output, scorer);
    }
View Full Code Here
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
        assert itemDomain.size() == itemDomain.domainSize();
        assert itemDomain.domainSize() == nitems;
        List<List<ScoredId>> matrix = Lists.newArrayListWithCapacity(itemDomain.domainSize());

        // working space for accumulating each row (reuse between rows)
        MutableSparseVector currentRow = MutableSparseVector.create(itemUniverse);
        Stopwatch timer = Stopwatch.createStarted();

        for (int i = 0; i < nitems; i++) {
            assert matrix.size() == i;
            final long rowItem = itemDomain.getKey(i);
            final SparseVector vec1 = buildContext.itemVector(rowItem);

            // Take advantage of sparsity if we can
            LongIterator neighbors = iterationStrategy.neighborIterator(buildContext, rowItem, false);
            currentRow.fill(0);

            // Compute similarities and populate the vector
            while (neighbors.hasNext()) {
                final long colItem = neighbors.nextLong();
                final SparseVector vec2 = buildContext.itemVector(colItem);
                assert currentRow.containsKey(colItem);
                currentRow.set(colItem, similarity.similarity(rowItem, vec1, colItem, vec2));
            }

            // Remove the current item (it is not its own neighbor)
            currentRow.unset(rowItem);

            // Normalize and truncate the row
            MutableSparseVector normalized = rowNormalizer.normalize(rowItem, currentRow, null);
            truncator.truncate(normalized);

            // Build up and save the row
            ScoredIdListBuilder bld = new ScoredIdListBuilder(normalized.size());
            // TODO Allow the symbols in use to be customized
            List<ScoredId> row = bld.addChannels(normalized.getChannelVectorSymbols())
                                    .addTypedChannels(normalized.getChannelSymbols())
                                    .addAll(ScoredIds.collectionFromVector(normalized))
                                    .sort(ScoredIds.scoreOrder().reverse())
                                    .finish();
            matrix.add(row);
        }
View Full Code Here
114
115
116
117
118
119
120
121
122
123
124
        Cursor<UserHistory<Event>> users = userEventDAO.streamEventsByUser();
        try {
            for (UserHistory<Event> user : users) {
                long uid = user.getUserId();
                SparseVector summary = userSummarizer.summarize(user);
                MutableSparseVector normed = summary.mutableCopy();
                normalizer.normalize(uid, summary, normed);

                for (VectorEntry rating : normed) {
                    final long item = rating.getKey();
                    // get the item's rating accumulator
View Full Code Here
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
     *
     * @return A sparse vector containing the data accumulated.
     */
    @SuppressWarnings({"rawtypes", "unchecked"})
    public ImmutableSparseVector buildVector() {
        MutableSparseVector msv = MutableSparseVector.create(ids);
        final int size = size();
        for (int i = 0; i < size; i++) {
            msv.set(ids.get(i), scores.get(i));
        }

        for (ChannelStorage chan: channels.values()) {
            MutableSparseVector vchan = msv.getOrAddChannelVector(chan.symbol);
            for (int i = 0; i < size; i++) {
                vchan.set(ids.get(i), chan.values.get(i));
            }
        }

        for (TypedChannelStorage<?> chan: typedChannels.values()) {
            Long2ObjectMap vchan = msv.getOrAddChannel(chan.symbol);
            for (int i = 0; i < size; i++) {
                vchan.put(ids.get(i), chan.values.get(i));
            }
        }

        return msv.freeze();
    }
View Full Code Here
146
147
148
149
150
151
152
153
154
155
            }

            logger.info("trained baseline on {} ratings in {} iterations (final rmse={})", ratings.size(), trainingController.getIterationCount(), rmse);

            // Convert the uoff array to a SparseVector
            MutableSparseVector svuoff = Vectors.fromArray(snapshot.userIndex(), uoff);
            // Convert the ioff array to a SparseVector
            MutableSparseVector svioff = Vectors.fromArray(snapshot.itemIndex(), ioff);
            return new LeastSquaresItemScorer(svuoff.freeze(), svioff.freeze(), mean);
        }
View Full Code Here
57
58
59
60
61
62
63
64
65
66
67
    public void score(long user, @Nonnull MutableSparseVector output) {
        primaryScorer.score(user, output);
        LongSet fallbackKeys = LongSets.EMPTY_SET;
        if (output.size() != output.keyDomain().size()) {
            fallbackKeys = output.unsetKeySet();
            MutableSparseVector blpreds = MutableSparseVector.create(fallbackKeys);
            baselineScorer.score(user, blpreds);
            output.set(blpreds);
        }

        // FIXME Make this faster
View Full Code Here
56
57
58
59
60
61
62
63
64
65
66
67
68
69
            return Double.NaN;
        }

        LongSet ts = LongUtils.setUnion(vec1.keySet(),vec2.keySet());

        MutableSparseVector v1 = MutableSparseVector.create(ts);
        v1.fill(0);
        v1.set(vec1);
        v1.multiply(1.0 / v1.norm());
        v1.addScaled(vec2, -1.0 / vec2.norm());

        distance = v1.norm();
        return 1-distance;
    }
View Full Code Here
79
80
81
82
83
84
85
86
87
88
89
90
91
92
    public void score(long user, @Nonnull MutableSparseVector items) {
        UserHistory<?> history = userEventDAO.getEventsForUser(user, summarizer.eventTypeWanted());
        if (history == null) {
            baseline.score(user, items);
        } else {
            MutableSparseVector vec = summarizer.summarize(history).mutableCopy();
            // score everything, both rated and not, for offsets
            LongSet allItems = LongUtils.setUnion(vec.keySet(), items.keyDomain());
            MutableSparseVector baseScores = MutableSparseVector.create(allItems);
            baseline.score(user, baseScores);
            // subtract scores from ratings, yielding offsets
            vec.subtract(baseScores);
            double meanOffset = vec.sum() / (vec.size() + damping);
            // to score: fill with baselines, add user mean offset
View Full Code Here
TOP

Related Classes of org.grouplens.lenskit.vectors.MutableSparseVector

  • org.grouplens.lenskit.baseline.ConstantItemScorerTest
  • org.grouplens.lenskit.baseline.FallbackItemScorer
  • org.grouplens.lenskit.baseline.FallbackItemScorerTest
  • org.grouplens.lenskit.baseline.LeastSquaresItemScorer$Builder
  • org.grouplens.lenskit.baseline.LeastSquaresItemScorerTest
  • org.grouplens.lenskit.baseline.UserMeanItemScorer
  • org.grouplens.lenskit.basic.AbstractGlobalItemScorer
  • org.grouplens.lenskit.basic.AbstractItemScorer
  • org.grouplens.lenskit.basic.AbstractRatingPredictor
  • org.grouplens.lenskit.basic.PrecomputedItemScorerTest
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.