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

Example 1 with Dataset

use of org.apache.ignite.ml.dataset.Dataset in project ignite by apache.

the class ANNClassificationTrainer method getCentroidStat.

/**
 */
private <K, V> CentroidStat getCentroidStat(DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> vectorizer, List<Vector> centers) {
    PartitionDataBuilder<K, V, EmptyContext, LabeledVectorSet<LabeledVector>> partDataBuilder = new LabeledDatasetPartitionDataBuilderOnHeap<>(vectorizer);
    try (Dataset<EmptyContext, LabeledVectorSet<LabeledVector>> dataset = datasetBuilder.build(envBuilder, (env, upstream, upstreamSize) -> new EmptyContext(), partDataBuilder, learningEnvironment())) {
        return dataset.compute(data -> {
            CentroidStat res = new CentroidStat();
            for (int i = 0; i < data.rowSize(); i++) {
                final IgniteBiTuple<Integer, Double> closestCentroid = findClosestCentroid(centers, data.getRow(i));
                int centroidIdx = closestCentroid.get1();
                double lb = data.label(i);
                // add new label to label set
                res.labels().add(lb);
                ConcurrentHashMap<Double, Integer> centroidStat = res.centroidStat.get(centroidIdx);
                if (centroidStat == null) {
                    centroidStat = new ConcurrentHashMap<>();
                    centroidStat.put(lb, 1);
                    res.centroidStat.put(centroidIdx, centroidStat);
                } else {
                    int cnt = centroidStat.getOrDefault(lb, 0);
                    centroidStat.put(lb, cnt + 1);
                }
                res.counts.merge(centroidIdx, 1, (IgniteBiFunction<Integer, Integer, Integer>) (i1, i2) -> i1 + i2);
            }
            return res;
        }, (a, b) -> {
            if (a == null)
                return b == null ? new CentroidStat() : b;
            if (b == null)
                return a;
            return a.merge(b);
        });
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
}

Example 2 with Dataset

use of org.apache.ignite.ml.dataset.Dataset in project ignite by apache.

the class KNNUtils method buildDataset.

/**
 * Builds dataset.
 *
 * @param envBuilder Learning environment builder.
 * @param datasetBuilder Dataset builder.
 * @param vectorizer Upstream vectorizer.
 * @return Dataset.
 */
@Nullable
public static <K, V, C extends Serializable> Dataset<EmptyContext, LabeledVectorSet<LabeledVector>> buildDataset(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> vectorizer) {
    LearningEnvironment environment = envBuilder.buildForTrainer();
    environment.initDeployingContext(vectorizer);
    PartitionDataBuilder<K, V, EmptyContext, LabeledVectorSet<LabeledVector>> partDataBuilder = new LabeledDatasetPartitionDataBuilderOnHeap<>(vectorizer);
    Dataset<EmptyContext, LabeledVectorSet<LabeledVector>> dataset = null;
    if (datasetBuilder != null) {
        dataset = datasetBuilder.build(envBuilder, (env, upstream, upstreamSize) -> new EmptyContext(), partDataBuilder, environment);
    }
    return dataset;
}

Example 3 with Dataset

use of org.apache.ignite.ml.dataset.Dataset in project ignite by apache.

the class LinearRegressionSGDTrainer method updateModel.

/**
 * {@inheritDoc}
 */
@Override
protected <K, V> LinearRegressionModel updateModel(LinearRegressionModel mdl, DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> extractor) {
    assert updatesStgy != null;
    IgniteFunction<Dataset<EmptyContext, SimpleLabeledDatasetData>, MLPArchitecture> archSupplier = dataset -> {
        int cols = dataset.compute(data -> {
            if (data.getFeatures() == null)
                return null;
            return data.getFeatures().length / data.getRows();
        }, (a, b) -> {
            if (a == null)
                return b == null ? 0 : b;
            if (b == null)
                return a;
            return b;
        });
        MLPArchitecture architecture = new MLPArchitecture(cols);
        architecture = architecture.withAddedLayer(1, true, Activators.LINEAR);
        return architecture;
    };
    MLPTrainer<?> trainer = new MLPTrainer<>(archSupplier, LossFunctions.MSE, updatesStgy, maxIterations, batchSize, locIterations, seed);
    IgniteFunction<LabeledVector<Double>, LabeledVector<double[]>> func = lv -> new LabeledVector<>(lv.features(), new double[] { lv.label() });
    PatchedPreprocessor<K, V, Double, double[]> patchedPreprocessor = new PatchedPreprocessor<>(func, extractor);
    MultilayerPerceptron mlp = Optional.ofNullable(mdl).map(this::restoreMLPState).map(m -> trainer.update(m, datasetBuilder, patchedPreprocessor)).orElseGet(() -> trainer.fit(datasetBuilder, patchedPreprocessor));
    double[] p = mlp.parameters().getStorage().data();
    return new LinearRegressionModel(new DenseVector(Arrays.copyOf(p, p.length - 1)), p[p.length - 1]);
}

Example 4 with Dataset

use of org.apache.ignite.ml.dataset.Dataset in project ignite by apache.

the class Evaluator method initEvaluationContexts.

/**
 * Inits evaluation contexts for metrics.
 *
 * @param dataset Dataset.
 * @param metrics Metrics.
 * @return Computed contexts.
 */
@SuppressWarnings("unchecked")
private static Map<Class, EvaluationContext> initEvaluationContexts(Dataset<EmptyContext, FeatureMatrixWithLabelsOnHeapData> dataset, Metric... metrics) {
    long nonEmptyCtxsCnt = Arrays.stream(metrics).map(x -> x.makeAggregator().createInitializedContext()).filter(x -> ((EvaluationContext) x).needToCompute()).count();
    if (nonEmptyCtxsCnt == 0) {
        HashMap<Class, EvaluationContext> res = new HashMap<>();
        for (Metric m : metrics) {
            MetricStatsAggregator<Double, ?, ?> aggregator = m.makeAggregator();
            res.put(aggregator.getClass(), (EvaluationContext) m.makeAggregator().createInitializedContext());
            return res;
        }
    }
    return dataset.compute(data -> {
        Map<Class, MetricStatsAggregator> aggrs = new HashMap<>();
        for (Metric m : metrics) {
            MetricStatsAggregator<Double, ?, ?> aggregator = m.makeAggregator();
            if (!aggrs.containsKey(aggregator.getClass()))
                aggrs.put(aggregator.getClass(), aggregator);
        }
        Map<Class, EvaluationContext> aggrToEvCtx = new HashMap<>();
        aggrs.forEach((clazz, aggr) -> aggrToEvCtx.put(clazz, (EvaluationContext) aggr.createInitializedContext()));
        for (int i = 0; i < data.getLabels().length; i++) {
            LabeledVector<Double> vector = VectorUtils.of(data.getFeatures()[i]).labeled(data.getLabels()[i]);
            aggrToEvCtx.values().forEach(ctx -> ctx.aggregate(vector));
        }
        return aggrToEvCtx;
    }, (left, right) -> {
        if (left == null && right == null)
            return new HashMap<>();
        if (left == null)
            return right;
        if (right == null)
            return left;
        HashMap<Class, EvaluationContext> res = new HashMap<>();
        for (Class key : left.keySet()) {
            EvaluationContext ctx1 = left.get(key);
            EvaluationContext ctx2 = right.get(key);
            A.ensure(ctx1 != null && ctx2 != null, "ctx1 != null && ctx2 != null");
            res.put(key, ctx1.mergeWith(ctx2));
        }
        return res;
    });
}

Example 5 with Dataset

use of org.apache.ignite.ml.dataset.Dataset in project ignite by apache.

the class KMeansTrainer method updateModel.

/**
 * {@inheritDoc}
 */
@Override
protected <K, V> KMeansModel updateModel(KMeansModel mdl, DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> preprocessor) {
    assert datasetBuilder != null;
    PartitionDataBuilder<K, V, EmptyContext, LabeledVectorSet<LabeledVector>> partDataBuilder = new LabeledDatasetPartitionDataBuilderOnHeap<>(preprocessor);
    Vector[] centers;
    try (Dataset<EmptyContext, LabeledVectorSet<LabeledVector>> dataset = datasetBuilder.build(envBuilder, (env, upstream, upstreamSize) -> new EmptyContext(), partDataBuilder, learningEnvironment())) {
        final Integer cols = dataset.compute(org.apache.ignite.ml.structures.Dataset::colSize, (a, b) -> {
            if (a == null)
                return b == null ? 0 : b;
            if (b == null)
                return a;
            return b;
        });
        if (cols == null)
            return getLastTrainedModelOrThrowEmptyDatasetException(mdl);
        centers = Optional.ofNullable(mdl).map(KMeansModel::centers).orElseGet(() -> initClusterCentersRandomly(dataset, k));
        boolean converged = false;
        int iteration = 0;
        while (iteration < maxIterations && !converged) {
            Vector[] newCentroids = new DenseVector[k];
            TotalCostAndCounts totalRes = calcDataForNewCentroids(centers, dataset, cols);
            converged = true;
            for (Map.Entry<Integer, Vector> entry : totalRes.sums.entrySet()) {
                Vector massCenter = entry.getValue().times(1.0 / totalRes.counts.get(entry.getKey()));
                if (converged && distance.compute(massCenter, centers[entry.getKey()]) > epsilon * epsilon)
                    converged = false;
                newCentroids[entry.getKey()] = massCenter;
            }
            iteration++;
            for (int i = 0; i < centers.length; i++) {
                if (newCentroids[i] != null)
                    centers[i] = newCentroids[i];
            }
        }
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
    return new KMeansModel(centers, distance);
}