Examples with PartitionDataBuilder org.apache.ignite.ml.dataset.PartitionDataBuilder used on opensource projects

Example 6 with PartitionDataBuilder

use of org.apache.ignite.ml.dataset.PartitionDataBuilder 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);
}