Examples with DatasetTrainer org.apache.ignite.ml.trainers.DatasetTrainer used on opensource projects

Example 1 with DatasetTrainer

use of org.apache.ignite.ml.trainers.DatasetTrainer in project ignite by apache.

the class RandomForestClassifierTrainerTest method testUpdate.

/**
 */
@Test
public void testUpdate() {
    int sampleSize = 1000;
    Map<Integer, LabeledVector<Double>> sample = new HashMap<>();
    for (int i = 0; i < sampleSize; i++) {
        double x1 = i;
        double x2 = x1 / 10.0;
        double x3 = x2 / 10.0;
        double x4 = x3 / 10.0;
        sample.put(i, VectorUtils.of(x1, x2, x3, x4).labeled((double) i % 2));
    }
    ArrayList<FeatureMeta> meta = new ArrayList<>();
    for (int i = 0; i < 4; i++) meta.add(new FeatureMeta("", i, false));
    DatasetTrainer<RandomForestModel, Double> trainer = new RandomForestClassifierTrainer(meta).withAmountOfTrees(100).withFeaturesCountSelectionStrgy(x -> 2).withEnvironmentBuilder(TestUtils.testEnvBuilder());
    RandomForestModel originalMdl = trainer.fit(sample, parts, new LabeledDummyVectorizer<>());
    RandomForestModel updatedOnSameDS = trainer.update(originalMdl, sample, parts, new LabeledDummyVectorizer<>());
    RandomForestModel updatedOnEmptyDS = trainer.update(originalMdl, new HashMap<Integer, LabeledVector<Double>>(), parts, new LabeledDummyVectorizer<>());
    Vector v = VectorUtils.of(5, 0.5, 0.05, 0.005);
    assertEquals(originalMdl.predict(v), updatedOnSameDS.predict(v), 0.01);
    assertEquals(originalMdl.predict(v), updatedOnEmptyDS.predict(v), 0.01);
}

Example 2 with DatasetTrainer

use of org.apache.ignite.ml.trainers.DatasetTrainer 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);
}