Package org.grouplens.lenskit.core

Examples of org.grouplens.lenskit.core.LenskitRecommender

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            } catch (InjectionException e) {
                logger.error("Error encountered while pre-processing algorithm components for sharing", e);
                throw new RecommenderBuildException("Pre-processing of algorithm components for sharing failed.", e);
            }
        }
        recommender = new LenskitRecommender(graph);
        // pre-fetch the test DAO
        userEvents = dataSet.getTestData().getUserEventDAO();
    }
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                   instanceOf(TopNItemRecommender.class));
    }

    @Test
    public void testConfigSeparation() {
        LenskitRecommender rec1 = null;
        LenskitRecommender rec2 = null;
        rec1 = engine.createRecommender();
        rec2 = engine.createRecommender();

        assertThat(rec1.getItemScorer(),
                   not(sameInstance(rec2.getItemScorer())));
        assertThat(rec1.get(SlopeOneModel.class),
                   allOf(not(nullValue()),
                         sameInstance(rec2.get(SlopeOneModel.class))));
    }
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            context.put("lenskit.eval.command.class", getName());
            context.put("lenskit.eval.command.name", getName());
            context.put("lenskit.eval.algorithm.name", algorithm.getName());

            // TODO Support serializing the recommender
            LenskitRecommender rec;
            StopWatch timer = new StopWatch();
            timer.start();
            try {
                logger.info("{}: building recommender {}", getName(), algorithm.getName());
                LenskitConfiguration config = new LenskitConfiguration();
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                instanceOf(ItemItemGlobalScorer.class));
    }

    @Test
    public void testConfigSeparation() {
        LenskitRecommender rec1 = null;
        LenskitRecommender rec2 = null;
        rec1 = engine.createRecommender();
        rec2 = engine.createRecommender();

        assertThat(rec1.getItemScorer(),
                   not(sameInstance(rec2.getItemScorer())));
        assertThat(rec1.get(ItemItemModel.class),
                   allOf(not(nullValue()),
                         sameInstance(rec2.get(ItemItemModel.class))));
    }
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    /**
     * Cache loader to extract the item universe from a recommender.
     */
    private static class UniverseLoader extends CacheLoader<Recommender,LongSet> {
        public LongSet load(Recommender rec) throws Exception {
            LenskitRecommender lkrec = (LenskitRecommender) rec;
            ItemDAO idao = lkrec.get(ItemDAO.class);
            if (idao == null) {
                logger.warn("Recommender has no item DAO");
                return LongSets.EMPTY_SET;
            } else {
                return idao.getItemIds();
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            count = n;
        }

        @Override
        public LongSet select(TestUser user) {
            LenskitRecommender lkr = (LenskitRecommender) user.getRecommender();
            Random rng = null;
            if (lkr != null) {
                rng = lkr.get(Random.class);
            }
            if (rng == null) {
                rng = new Random();
            }
            LongSet items = delegate.select(user);
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        @Override
        public LongSet apply(@Nullable Recommender input) {
            if (input == null) {
                return LongSets.EMPTY_SET;
            }
            LenskitRecommender rec = (LenskitRecommender) input;
            ItemEventDAO idao = rec.get(ItemEventDAO.class);
            ScoredItemAccumulator accum = new TopNScoredItemAccumulator(count);
            Cursor<ItemEventCollection<Event>> items = idao.streamEventsByItem();
            try {
                for (ItemEventCollection<Event> item: items) {
                    accum.put(item.getItemId(), item.size());
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    }

    @Test
    public void testFeatureInfo() throws RecommenderBuildException {
        LenskitRecommenderEngine engine = makeEngine();
        LenskitRecommender rec = engine.createRecommender();

        FunkSVDModel model = rec.get(FunkSVDModel.class);
        assertThat(model, notNullValue());
        assertThat(model.getFeatureInfo().size(),
                   equalTo(20));
        for (FeatureInfo feat: model.getFeatureInfo()) {
            assertThat(feat.getIterCount(), equalTo(10));
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                   instanceOf(ItemItemGlobalScorer.class));
    }

    @Test
    public void testContextRemoved() {
        LenskitRecommender rec = engine.createRecommender();
        assertThat(rec.get(ItemItemBuildContext.class),
                   nullValue());
    }
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                   nullValue());
    }

    @Test
    public void testConfigSeparation() {
        LenskitRecommender rec1 = null;
        LenskitRecommender rec2 = null;
        rec1 = engine.createRecommender();
        rec2 = engine.createRecommender();

        assertThat(rec1.getItemScorer(),
                   not(sameInstance(rec2.getItemScorer())));
        assertThat(rec1.get(ItemItemModel.class),
                   allOf(not(nullValue()),
                         sameInstance(rec2.get(ItemItemModel.class))));
    }
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Related Classes of org.grouplens.lenskit.core.LenskitRecommender

  • org.grouplens.lenskit.eval.metrics.topn.ItemSelectors$PopularItemSelector
  • org.grouplens.lenskit.eval.metrics.topn.ItemSelectors$RandomSubsetItemSelector
  • org.grouplens.lenskit.eval.metrics.topn.ItemSelectors$UniverseLoader
  • org.grouplens.lenskit.eval.TrainModelTask
  • org.grouplens.lenskit.eval.traintest.LenskitEvalJob
  • org.grouplens.lenskit.inject.StaticInjector
  • org.grouplens.lenskit.knn.item.ItemItemRecommenderBuildTest
  • org.grouplens.lenskit.knn.item.ItemwiseRecommenderBuildTest
  • org.grouplens.lenskit.knn.item.model.NormalizingItemItemRecommenderBuildTest
  • org.grouplens.lenskit.mf.funksvd.FunkSVDRecommenderBuildTest
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