Examples with EmptyContextBuilder org.apache.ignite.ml.dataset.primitive.builder.context.EmptyContextBuilder used on opensource projects

Example 1 with EmptyContextBuilder

use of org.apache.ignite.ml.dataset.primitive.builder.context.EmptyContextBuilder in project ignite by apache.

the class DataStreamGeneratorFillCacheTest method testCacheFilling.

/**
 */
@Test
public void testCacheFilling() {
    IgniteConfiguration configuration = new IgniteConfiguration().setDiscoverySpi(new TcpDiscoverySpi().setIpFinder(new TcpDiscoveryVmIpFinder().setAddresses(Arrays.asList("127.0.0.1:47500..47509"))));
    String cacheName = "TEST_CACHE";
    CacheConfiguration<UUID, LabeledVector<Double>> cacheConfiguration = new CacheConfiguration<UUID, LabeledVector<Double>>(cacheName).setAffinity(new RendezvousAffinityFunction(false, 10));
    int datasetSize = 5000;
    try (Ignite ignite = Ignition.start(configuration)) {
        IgniteCache<UUID, LabeledVector<Double>> cache = ignite.getOrCreateCache(cacheConfiguration);
        DataStreamGenerator generator = new GaussRandomProducer(0).vectorize(1).asDataStream();
        generator.fillCacheWithVecUUIDAsKey(datasetSize, cache);
        LabeledDummyVectorizer<UUID, Double> vectorizer = new LabeledDummyVectorizer<>();
        CacheBasedDatasetBuilder<UUID, LabeledVector<Double>> datasetBuilder = new CacheBasedDatasetBuilder<>(ignite, cache);
        IgniteFunction<SimpleDatasetData, StatPair> map = data -> new StatPair(DoubleStream.of(data.getFeatures()).sum(), data.getRows());
        LearningEnvironment env = LearningEnvironmentBuilder.defaultBuilder().buildForTrainer();
        env.deployingContext().initByClientObject(map);
        try (CacheBasedDataset<UUID, LabeledVector<Double>, EmptyContext, SimpleDatasetData> dataset = datasetBuilder.build(LearningEnvironmentBuilder.defaultBuilder(), new EmptyContextBuilder<>(), new SimpleDatasetDataBuilder<>(vectorizer), env)) {
            StatPair res = dataset.compute(map, StatPair::sum);
            assertEquals(datasetSize, res.cntOfRows);
            assertEquals(0.0, res.elementsSum / res.cntOfRows, 1e-2);
        }
        ignite.destroyCache(cacheName);
    }
}

Example 2 with EmptyContextBuilder

use of org.apache.ignite.ml.dataset.primitive.builder.context.EmptyContextBuilder in project ignite by apache.

the class LearningEnvironmentTest method testRandomNumbersGenerator.

/**
 * Test random number generator provided by  {@link LearningEnvironment}.
 * We test that:
 * 1. Correct random generator is returned for each partition.
 * 2. Its state is saved between compute calls (for this we do several iterations of compute).
 */
@Test
public void testRandomNumbersGenerator() {
    // We make such builders that provide as functions returning partition index * iteration as random number generator nextInt
    LearningEnvironmentBuilder envBuilder = TestUtils.testEnvBuilder().withRandomDependency(MockRandom::new);
    int partitions = 10;
    int iterations = 2;
    DatasetTrainer<IgniteModel<Object, Vector>, Void> trainer = new DatasetTrainer<IgniteModel<Object, Vector>, Void>() {

        /**
         * {@inheritDoc}
         */
        @Override
        public <K, V> IgniteModel<Object, Vector> fitWithInitializedDeployingContext(DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> preprocessor) {
            Dataset<EmptyContext, TestUtils.DataWrapper<Integer>> ds = datasetBuilder.build(envBuilder, new EmptyContextBuilder<>(), (PartitionDataBuilder<K, V, EmptyContext, TestUtils.DataWrapper<Integer>>) (env, upstreamData, upstreamDataSize, ctx) -> TestUtils.DataWrapper.of(env.partition()), envBuilder.buildForTrainer());
            Vector v = null;
            for (int iter = 0; iter < iterations; iter++) {
                v = ds.compute((dw, env) -> VectorUtils.fill(-1, partitions).set(env.partition(), env.randomNumbersGenerator().nextInt()), (v1, v2) -> zipOverridingEmpty(v1, v2, -1));
            }
            return constantModel(v);
        }

        /**
         * {@inheritDoc}
         */
        @Override
        public boolean isUpdateable(IgniteModel<Object, Vector> mdl) {
            return false;
        }

        /**
         * {@inheritDoc}
         */
        @Override
        protected <K, V> IgniteModel<Object, Vector> updateModel(IgniteModel<Object, Vector> mdl, DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> preprocessor) {
            return null;
        }
    };
    trainer.withEnvironmentBuilder(envBuilder);
    IgniteModel<Object, Vector> mdl = trainer.fit(getCacheMock(partitions), partitions, null);
    Vector exp = VectorUtils.zeroes(partitions);
    for (int i = 0; i < partitions; i++) exp.set(i, i * iterations);
    Vector res = mdl.predict(null);
    assertEquals(exp, res);
}