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

Example 6 with Preprocessor

use of org.apache.ignite.ml.preprocessing.Preprocessor 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 7 with Preprocessor

use of org.apache.ignite.ml.preprocessing.Preprocessor 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 8 with Preprocessor

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

the class MinMaxScalerTrainer method fit.

/**
 * {@inheritDoc}
 */
@Override
public MinMaxScalerPreprocessor<K, V> fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> basePreprocessor) {
    PartitionContextBuilder<K, V, EmptyContext> ctxBuilder = (env, upstream, upstreamSize) -> new EmptyContext();
    try (Dataset<EmptyContext, MinMaxScalerPartitionData> dataset = datasetBuilder.build(envBuilder, ctxBuilder, (env, upstream, upstreamSize, ctx) -> {
        double[] min = null;
        double[] max = null;
        while (upstream.hasNext()) {
            UpstreamEntry<K, V> entity = upstream.next();
            LabeledVector row = basePreprocessor.apply(entity.getKey(), entity.getValue());
            if (min == null) {
                min = new double[row.size()];
                Arrays.fill(min, Double.MAX_VALUE);
            } else
                assert min.length == row.size() : "Base preprocessor must return exactly " + min.length + " features";
            if (max == null) {
                max = new double[row.size()];
                Arrays.fill(max, -Double.MAX_VALUE);
            } else
                assert max.length == row.size() : "Base preprocessor must return exactly " + min.length + " features";
            for (int i = 0; i < row.size(); i++) {
                if (row.get(i) < min[i])
                    min[i] = row.get(i);
                if (row.get(i) > max[i])
                    max[i] = row.get(i);
            }
        }
        return new MinMaxScalerPartitionData(min, max);
    }, learningEnvironment(basePreprocessor))) {
        double[][] minMax = dataset.compute(data -> data.getMin() != null ? new double[][] { data.getMin(), data.getMax() } : null, (a, b) -> {
            if (a == null)
                return b;
            if (b == null)
                return a;
            double[][] res = new double[2][];
            res[0] = new double[a[0].length];
            for (int i = 0; i < res[0].length; i++) res[0][i] = Math.min(a[0][i], b[0][i]);
            res[1] = new double[a[1].length];
            for (int i = 0; i < res[1].length; i++) res[1][i] = Math.max(a[1][i], b[1][i]);
            return res;
        });
        return new MinMaxScalerPreprocessor<>(minMax[0], minMax[1], basePreprocessor);
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
}

Example 9 with Preprocessor

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

the class LogisticRegressionSGDTrainer method updateModel.

/**
 * {@inheritDoc}
 */
@Override
protected <K, V> LogisticRegressionModel updateModel(LogisticRegressionModel mdl, DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> extractor) {
    IgniteFunction<Dataset<EmptyContext, SimpleLabeledDatasetData>, MLPArchitecture> archSupplier = dataset -> {
        Integer cols = dataset.compute(data -> {
            if (data.getFeatures() == null)
                return null;
            return data.getFeatures().length / data.getRows();
        }, (a, b) -> {
            // If both are null then zero will be propagated, no good.
            if (a == null)
                return b;
            return a;
        });
        if (cols == null)
            throw new IllegalStateException("Cannot train on empty dataset");
        MLPArchitecture architecture = new MLPArchitecture(cols);
        architecture = architecture.withAddedLayer(1, true, Activators.SIGMOID);
        return architecture;
    };
    MLPTrainer<?> trainer = new MLPTrainer<>(archSupplier, LossFunctions.L2, updatesStgy, maxIterations, batchSize, locIterations, seed).withEnvironmentBuilder(envBuilder);
    MultilayerPerceptron mlp;
    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);
    if (mdl != null) {
        mlp = restoreMLPState(mdl);
        mlp = trainer.update(mlp, datasetBuilder, patchedPreprocessor);
    } else
        mlp = trainer.fit(datasetBuilder, patchedPreprocessor);
    double[] params = mlp.parameters().getStorage().data();
    return new LogisticRegressionModel(new DenseVector(Arrays.copyOf(params, params.length - 1)), params[params.length - 1]);
}

Example 10 with Preprocessor

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

the class Deltas method updateModel.

/**
 * {@inheritDoc}
 */
@Override
protected <K, V> SVMLinearClassificationModel updateModel(SVMLinearClassificationModel mdl, DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> preprocessor) {
    assert datasetBuilder != null;
    IgniteFunction<Double, Double> lbTransformer = lb -> {
        if (lb == 0.0)
            return -1.0;
        else
            return lb;
    };
    IgniteFunction<LabeledVector<Double>, LabeledVector<Double>> func = lv -> new LabeledVector<>(lv.features(), lbTransformer.apply(lv.label()));
    PatchedPreprocessor<K, V, Double, Double> patchedPreprocessor = new PatchedPreprocessor<>(func, preprocessor);
    PartitionDataBuilder<K, V, EmptyContext, LabeledVectorSet<LabeledVector>> partDataBuilder = new LabeledDatasetPartitionDataBuilderOnHeap<>(patchedPreprocessor);
    Vector weights;
    try (Dataset<EmptyContext, LabeledVectorSet<LabeledVector>> dataset = datasetBuilder.build(envBuilder, (env, upstream, upstreamSize) -> new EmptyContext(), partDataBuilder, learningEnvironment())) {
        if (mdl == null) {
            final int 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;
            });
            final int weightVectorSizeWithIntercept = cols + 1;
            weights = initializeWeightsWithZeros(weightVectorSizeWithIntercept);
        } else
            weights = getStateVector(mdl);
        for (int i = 0; i < this.getAmountOfIterations(); i++) {
            Vector deltaWeights = calculateUpdates(weights, dataset);
            if (deltaWeights == null)
                return getLastTrainedModelOrThrowEmptyDatasetException(mdl);
            // creates new vector
            weights = weights.plus(deltaWeights);
        }
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
    return new SVMLinearClassificationModel(weights.copyOfRange(1, weights.size()), weights.get(0));
}