Examples with Preprocessor org.apache.ignite.ml.preprocessing.Preprocessor used on opensource projects

Example 11 with Preprocessor

use of org.apache.ignite.ml.preprocessing.Preprocessor in project ignite by apache.

the class ImputerTrainer method fit.

/**
 * {@inheritDoc}
 */
@Override
public ImputerPreprocessor<K, V> fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> basePreprocessor) {
    PartitionContextBuilder<K, V, EmptyContext> builder = (env, upstream, upstreamSize) -> new EmptyContext();
    try (Dataset<EmptyContext, ImputerPartitionData> dataset = datasetBuilder.build(envBuilder, builder, (env, upstream, upstreamSize, ctx) -> {
        double[] sums = null;
        int[] counts = null;
        double[] maxs = null;
        double[] mins = null;
        Map<Double, Integer>[] valuesByFreq = null;
        while (upstream.hasNext()) {
            UpstreamEntry<K, V> entity = upstream.next();
            LabeledVector row = basePreprocessor.apply(entity.getKey(), entity.getValue());
            switch(imputingStgy) {
                case MEAN:
                    sums = updateTheSums(row, sums);
                    counts = updateTheCounts(row, counts);
                    break;
                case MOST_FREQUENT:
                    valuesByFreq = updateFrequenciesByGivenRow(row, valuesByFreq);
                    break;
                case LEAST_FREQUENT:
                    valuesByFreq = updateFrequenciesByGivenRow(row, valuesByFreq);
                    break;
                case MAX:
                    maxs = updateTheMaxs(row, maxs);
                    break;
                case MIN:
                    mins = updateTheMins(row, mins);
                    break;
                case COUNT:
                    counts = updateTheCounts(row, counts);
                    break;
                default:
                    throw new UnsupportedOperationException("The chosen strategy is not supported");
            }
        }
        ImputerPartitionData partData;
        switch(imputingStgy) {
            case MEAN:
                partData = new ImputerPartitionData().withSums(sums).withCounts(counts);
                break;
            case MOST_FREQUENT:
                partData = new ImputerPartitionData().withValuesByFrequency(valuesByFreq);
                break;
            case LEAST_FREQUENT:
                partData = new ImputerPartitionData().withValuesByFrequency(valuesByFreq);
                break;
            case MAX:
                partData = new ImputerPartitionData().withMaxs(maxs);
                break;
            case MIN:
                partData = new ImputerPartitionData().withMins(mins);
                break;
            case COUNT:
                partData = new ImputerPartitionData().withCounts(counts);
                break;
            default:
                throw new UnsupportedOperationException("The chosen strategy is not supported");
        }
        return partData;
    }, learningEnvironment(basePreprocessor))) {
        Vector imputingValues;
        switch(imputingStgy) {
            case MEAN:
                imputingValues = VectorUtils.of(calculateImputingValuesBySumsAndCounts(dataset));
                break;
            case MOST_FREQUENT:
                imputingValues = VectorUtils.of(calculateImputingValuesByTheMostFrequentValues(dataset));
                break;
            case LEAST_FREQUENT:
                imputingValues = VectorUtils.of(calculateImputingValuesByTheLeastFrequentValues(dataset));
                break;
            case MAX:
                imputingValues = VectorUtils.of(calculateImputingValuesByMaxValues(dataset));
                break;
            case MIN:
                imputingValues = VectorUtils.of(calculateImputingValuesByMinValues(dataset));
                break;
            case COUNT:
                imputingValues = VectorUtils.of(calculateImputingValuesByCounts(dataset));
                break;
            default:
                throw new UnsupportedOperationException("The chosen strategy is not supported");
        }
        return new ImputerPreprocessor<>(imputingValues, basePreprocessor);
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
}

Example 12 with Preprocessor

use of org.apache.ignite.ml.preprocessing.Preprocessor in project ignite by apache.

the class MLDeployingTest method createPreprocessor.

/**
 */
private Preprocessor<Integer, Vector> createPreprocessor(Preprocessor<Integer, Vector> basePreprocessor, String clsName) throws Exception {
    ClassLoader ldr = getExternalClassLoader();
    Class<?> clazz = ldr.loadClass(clsName);
    Constructor ctor = clazz.getConstructor(Preprocessor.class);
    return (Preprocessor<Integer, Vector>) ctor.newInstance(basePreprocessor);
}

Example 13 with Preprocessor

use of org.apache.ignite.ml.preprocessing.Preprocessor 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);
}