Examples with LearningEnvironment org.apache.ignite.ml.environment.LearningEnvironment used on opensource projects

Example 11 with LearningEnvironment

use of org.apache.ignite.ml.environment.LearningEnvironment in project ignite by apache.

the class ComputeUtils method getData.

/**
 * Extracts partition {@code data} from the local storage, if it's not found in local storage recovers this {@code
 * data} from a partition {@code upstream} and {@code context}. Be aware that this method should be called from
 * the node where partition is placed.
 *
 * @param ignite Ignite instance.
 * @param upstreamCacheName Name of an {@code upstream} cache.
 * @param filter Filter for {@code upstream} data.
 * @param transformerBuilder Builder of upstream transformers.
 * @param datasetCacheName Name of a partition {@code context} cache.
 * @param datasetId Dataset ID.
 * @param partDataBuilder Partition data builder.
 * @param env Learning environment.
 * @param <K> Type of a key in {@code upstream} data.
 * @param <V> Type of a value in {@code upstream} data.
 * @param <C> Type of a partition {@code context}.
 * @param <D> Type of a partition {@code data}.
 * @return Partition {@code data}.
 */
public static <K, V, C extends Serializable, D extends AutoCloseable> D getData(Ignite ignite, String upstreamCacheName, IgniteBiPredicate<K, V> filter, UpstreamTransformerBuilder transformerBuilder, String datasetCacheName, UUID datasetId, PartitionDataBuilder<K, V, C, D> partDataBuilder, LearningEnvironment env, boolean isKeepBinary) {
    PartitionDataStorage dataStorage = (PartitionDataStorage) ignite.cluster().nodeLocalMap().computeIfAbsent(String.format(DATA_STORAGE_KEY_TEMPLATE, datasetId), key -> new PartitionDataStorage());
    final int part = env.partition();
    return dataStorage.computeDataIfAbsent(part, () -> {
        IgniteCache<Integer, C> learningCtxCache = ignite.cache(datasetCacheName);
        C ctx = learningCtxCache.get(part);
        IgniteCache<K, V> upstreamCache = ignite.cache(upstreamCacheName);
        if (isKeepBinary)
            upstreamCache = upstreamCache.withKeepBinary();
        ScanQuery<K, V> qry = new ScanQuery<>();
        qry.setLocal(true);
        qry.setPartition(part);
        qry.setFilter(filter);
        UpstreamTransformer transformer = transformerBuilder.build(env);
        UpstreamTransformer transformerCp = Utils.copy(transformer);
        long cnt = computeCount(upstreamCache, qry, transformer);
        if (cnt > 0) {
            try (QueryCursor<UpstreamEntry<K, V>> cursor = upstreamCache.query(qry, e -> new UpstreamEntry<>(e.getKey(), e.getValue()))) {
                Iterator<UpstreamEntry<K, V>> it = cursor.iterator();
                Stream<UpstreamEntry> transformedStream = transformerCp.transform(Utils.asStream(it, cnt).map(x -> (UpstreamEntry) x));
                it = Utils.asStream(transformedStream.iterator()).map(x -> (UpstreamEntry<K, V>) x).iterator();
                Iterator<UpstreamEntry<K, V>> iter = new IteratorWithConcurrentModificationChecker<>(it, cnt, "Cache expected to be not modified during dataset data building [partition=" + part + ']');
                return partDataBuilder.build(env, iter, cnt, ctx);
            }
        }
        return null;
    });
}

Example 12 with LearningEnvironment

use of org.apache.ignite.ml.environment.LearningEnvironment in project ignite by apache.

the class LocalDataset method computeWithCtx.

/**
 * {@inheritDoc}
 */
@Override
public <R> R computeWithCtx(IgniteTriFunction<C, D, LearningEnvironment, R> map, IgniteBinaryOperator<R> reduce, R identity) {
    R res = identity;
    for (int part = 0; part < ctx.size(); part++) {
        D partData = data.get(part);
        LearningEnvironment env = envs.get(part);
        if (partData != null)
            res = reduce.apply(res, map.apply(ctx.get(part), partData, env));
    }
    return res;
}

Example 13 with LearningEnvironment

use of org.apache.ignite.ml.environment.LearningEnvironment in project ignite by apache.

the class DatasetFactory method createSimpleDataset.

/**
 * Creates a new instance of distributed {@link SimpleDataset} using the specified {@code partCtxBuilder} and {@code
 * featureExtractor}. This methods determines partition {@code data} to be {@link SimpleDatasetData}, but allows to
 * use any desired type of partition {@code context}.
 *
 * @param datasetBuilder Dataset builder.
 * @param envBuilder Learning environment builder.
 * @param partCtxBuilder Partition {@code context} builder.
 * @param featureExtractor Feature extractor used to extract features and build {@link SimpleDatasetData}.
 * @param <K> Type of a key in {@code upstream} data.
 * @param <V> Type of a value in {@code upstream} data.
 * @param <C> Type of a partition {@code context}.
 * @return Dataset.
 */
public static <K, V, C extends Serializable, CO extends Serializable> SimpleDataset<C> createSimpleDataset(DatasetBuilder<K, V> datasetBuilder, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K, V, C> partCtxBuilder, Preprocessor<K, V> featureExtractor) {
    LearningEnvironment environment = LearningEnvironmentBuilder.defaultBuilder().buildForTrainer();
    environment.initDeployingContext(featureExtractor);
    return create(datasetBuilder, envBuilder, partCtxBuilder, new SimpleDatasetDataBuilder<>(featureExtractor), environment).wrap(SimpleDataset::new);
}

Example 14 with LearningEnvironment

use of org.apache.ignite.ml.environment.LearningEnvironment in project ignite by apache.

the class DatasetFactory method createSimpleLabeledDataset.

/**
 * Creates a new instance of distributed {@link SimpleLabeledDataset} using the specified {@code partCtxBuilder},
 * {@code featureExtractor} and {@code lbExtractor}. This method determines partition {@code data} to be {@link
 * SimpleLabeledDatasetData}, but allows to use any desired type of partition {@code context}.
 *
 * @param datasetBuilder Dataset builder.
 * @param envBuilder Learning environment builder.
 * @param partCtxBuilder Partition {@code context} builder.
 * @param vectorizer Upstream vectorizer used to extract features and labels and build {@link
 * SimpleLabeledDatasetData}.
 * @param <K> Type of a key in {@code upstream} data.
 * @param <V> Type of a value in {@code upstream} data.
 * @param <C> Type of a partition {@code context}.
 * @return Dataset.
 */
public static <K, V, C extends Serializable, CO extends Serializable> SimpleLabeledDataset<C> createSimpleLabeledDataset(DatasetBuilder<K, V> datasetBuilder, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K, V, C> partCtxBuilder, Preprocessor<K, V> vectorizer) {
    LearningEnvironment environment = LearningEnvironmentBuilder.defaultBuilder().buildForTrainer();
    environment.initDeployingContext(vectorizer);
    return create(datasetBuilder, envBuilder, partCtxBuilder, new SimpleLabeledDatasetDataBuilder<>(vectorizer), environment).wrap(SimpleLabeledDataset::new);
}

Example 15 with LearningEnvironment

use of org.apache.ignite.ml.environment.LearningEnvironment in project ignite by apache.

the class CacheBasedDataset method compute.

/**
 * {@inheritDoc}
 */
@Override
public <R> R compute(IgniteBiFunction<D, LearningEnvironment, R> map, IgniteBinaryOperator<R> reduce, R identity) {
    String upstreamCacheName = upstreamCache.getName();
    String datasetCacheName = datasetCache.getName();
    return computeForAllPartitions(part -> {
        LearningEnvironment env = ComputeUtils.getLearningEnvironment(Ignition.localIgnite(), datasetId, part, envBuilder);
        D data = ComputeUtils.getData(Ignition.localIgnite(), upstreamCacheName, filter, upstreamTransformerBuilder, datasetCacheName, datasetId, partDataBuilder, env, upstreamKeepBinary);
        return data != null ? map.apply(data, env) : null;
    }, reduce, identity);
}