Examples with IgniteFunction org.apache.ignite.ml.math.functions.IgniteFunction used on opensource projects

Example 11 with IgniteFunction

use of org.apache.ignite.ml.math.functions.IgniteFunction in project ignite by apache.

the class ColumnDecisionTreeTrainerBenchmark method tstF1.

/**
 * Test decision tree regression.
 * To run this test rename this method so it starts from 'test'.
 */
public void tstF1() {
    IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
    int ptsCnt = 10000;
    Map<Integer, double[]> ranges = new HashMap<>();
    ranges.put(0, new double[] { -100.0, 100.0 });
    ranges.put(1, new double[] { -100.0, 100.0 });
    ranges.put(2, new double[] { -100.0, 100.0 });
    int featCnt = 100;
    double[] defRng = { -1.0, 1.0 };
    Vector[] trainVectors = vecsFromRanges(ranges, featCnt, defRng, new Random(123L), ptsCnt, f1);
    SparseDistributedMatrix m = new SparseDistributedMatrix(ptsCnt, featCnt + 1, StorageConstants.COLUMN_STORAGE_MODE, StorageConstants.RANDOM_ACCESS_MODE);
    SparseDistributedMatrixStorage sto = (SparseDistributedMatrixStorage) m.getStorage();
    loadVectorsIntoSparseDistributedMatrixCache(sto.cache().getName(), sto.getUUID(), Arrays.stream(trainVectors).iterator(), featCnt + 1);
    IgniteFunction<DoubleStream, Double> regCalc = s -> s.average().orElse(0.0);
    ColumnDecisionTreeTrainer<VarianceSplitCalculator.VarianceData> trainer = new ColumnDecisionTreeTrainer<>(10, ContinuousSplitCalculators.VARIANCE, RegionCalculators.VARIANCE, regCalc, ignite);
    X.println("Training started.");
    long before = System.currentTimeMillis();
    DecisionTreeModel mdl = trainer.train(new MatrixColumnDecisionTreeTrainerInput(m, new HashMap<>()));
    X.println("Training finished in: " + (System.currentTimeMillis() - before) + " ms.");
    Vector[] testVectors = vecsFromRanges(ranges, featCnt, defRng, new Random(123L), 20, f1);
    IgniteTriFunction<Model<Vector, Double>, Stream<IgniteBiTuple<Vector, Double>>, Function<Double, Double>, Double> mse = Estimators.MSE();
    Double accuracy = mse.apply(mdl, Arrays.stream(testVectors).map(v -> new IgniteBiTuple<>(v.viewPart(0, featCnt), v.getX(featCnt))), Function.identity());
    X.println("MSE: " + accuracy);
}

Example 12 with IgniteFunction

use of org.apache.ignite.ml.math.functions.IgniteFunction in project ignite by apache.

the class GDBOnTreesRegressionExportImportExample method main.

/**
 * Run example.
 *
 * @param args Command line arguments, none required.
 */
public static void main(String[] args) throws IOException {
    System.out.println();
    System.out.println(">>> GDB regression trainer example started.");
    // Start ignite grid.
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Ignite grid started.");
        // Create cache with training data.
        CacheConfiguration<Integer, double[]> trainingSetCfg = createCacheConfiguration();
        IgniteCache<Integer, double[]> trainingSet = null;
        Path jsonMdlPath = null;
        try {
            trainingSet = fillTrainingData(ignite, trainingSetCfg);
            // Create regression trainer.
            GDBTrainer trainer = new GDBRegressionOnTreesTrainer(1.0, 2000, 1, 0.).withCheckConvergenceStgyFactory(new MeanAbsValueConvergenceCheckerFactory(0.001));
            // Train decision tree model.
            GDBModel mdl = trainer.fit(ignite, trainingSet, new DoubleArrayVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.LAST));
            System.out.println("\n>>> Exported GDB regression model: " + mdl.toString(true));
            predictOnGeneratedData(mdl);
            jsonMdlPath = Files.createTempFile(null, null);
            mdl.toJSON(jsonMdlPath);
            IgniteFunction<Double, Double> lbMapper = lb -> lb;
            GDBModel modelImportedFromJSON = GDBModel.fromJSON(jsonMdlPath).withLblMapping(lbMapper);
            System.out.println("\n>>> Imported GDB regression model: " + modelImportedFromJSON.toString(true));
            predictOnGeneratedData(modelImportedFromJSON);
            System.out.println(">>> GDB regression trainer example completed.");
        } finally {
            if (trainingSet != null)
                trainingSet.destroy();
            if (jsonMdlPath != null)
                Files.deleteIfExists(jsonMdlPath);
        }
    } finally {
        System.out.flush();
    }
}

Example 13 with IgniteFunction

use of org.apache.ignite.ml.math.functions.IgniteFunction 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 14 with IgniteFunction

use of org.apache.ignite.ml.math.functions.IgniteFunction 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));
}

Example 15 with IgniteFunction

use of org.apache.ignite.ml.math.functions.IgniteFunction in project ignite by apache.

the class DataStreamGeneratorFillCacheTest method testCacheFilling.

/**
 */
@Test
public void testCacheFilling() {
    IgniteConfiguration configuration = new IgniteConfiguration().setDiscoverySpi(new TcpDiscoverySpi().setIpFinder(new TcpDiscoveryVmIpFinder().setAddresses(Arrays.asList("127.0.0.1:47500..47509"))));
    String cacheName = "TEST_CACHE";
    CacheConfiguration<UUID, LabeledVector<Double>> cacheConfiguration = new CacheConfiguration<UUID, LabeledVector<Double>>(cacheName).setAffinity(new RendezvousAffinityFunction(false, 10));
    int datasetSize = 5000;
    try (Ignite ignite = Ignition.start(configuration)) {
        IgniteCache<UUID, LabeledVector<Double>> cache = ignite.getOrCreateCache(cacheConfiguration);
        DataStreamGenerator generator = new GaussRandomProducer(0).vectorize(1).asDataStream();
        generator.fillCacheWithVecUUIDAsKey(datasetSize, cache);
        LabeledDummyVectorizer<UUID, Double> vectorizer = new LabeledDummyVectorizer<>();
        CacheBasedDatasetBuilder<UUID, LabeledVector<Double>> datasetBuilder = new CacheBasedDatasetBuilder<>(ignite, cache);
        IgniteFunction<SimpleDatasetData, StatPair> map = data -> new StatPair(DoubleStream.of(data.getFeatures()).sum(), data.getRows());
        LearningEnvironment env = LearningEnvironmentBuilder.defaultBuilder().buildForTrainer();
        env.deployingContext().initByClientObject(map);
        try (CacheBasedDataset<UUID, LabeledVector<Double>, EmptyContext, SimpleDatasetData> dataset = datasetBuilder.build(LearningEnvironmentBuilder.defaultBuilder(), new EmptyContextBuilder<>(), new SimpleDatasetDataBuilder<>(vectorizer), env)) {
            StatPair res = dataset.compute(map, StatPair::sum);
            assertEquals(datasetSize, res.cntOfRows);
            assertEquals(0.0, res.elementsSum / res.cntOfRows, 1e-2);
        }
        ignite.destroyCache(cacheName);
    }
}