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

Example 1 with FeatureMatrixWithLabelsOnHeapData

use of org.apache.ignite.ml.dataset.primitive.FeatureMatrixWithLabelsOnHeapData in project ignite by apache.

the class MeanAbsValueConvergenceCheckerTest method testConvergenceChecking.

/**
 */
@Test
public void testConvergenceChecking() {
    LocalDatasetBuilder<Integer, LabeledVector<Double>> datasetBuilder = new LocalDatasetBuilder<>(data, 1);
    ConvergenceChecker<Integer, LabeledVector<Double>> checker = createChecker(new MeanAbsValueConvergenceCheckerFactory(0.1), datasetBuilder);
    double error = checker.computeError(VectorUtils.of(1, 2), 4.0, notConvergedMdl);
    LearningEnvironmentBuilder envBuilder = TestUtils.testEnvBuilder();
    Assert.assertEquals(1.9, error, 0.01);
    Assert.assertFalse(checker.isConverged(envBuilder, datasetBuilder, notConvergedMdl));
    Assert.assertTrue(checker.isConverged(envBuilder, datasetBuilder, convergedMdl));
    try (LocalDataset<EmptyContext, FeatureMatrixWithLabelsOnHeapData> dataset = datasetBuilder.build(envBuilder, new EmptyContextBuilder<>(), new FeatureMatrixWithLabelsOnHeapDataBuilder<>(vectorizer), envBuilder.buildForTrainer())) {
        double onDSError = checker.computeMeanErrorOnDataset(dataset, notConvergedMdl);
        Assert.assertEquals(1.55, onDSError, 0.01);
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
}

Example 2 with FeatureMatrixWithLabelsOnHeapData

use of org.apache.ignite.ml.dataset.primitive.FeatureMatrixWithLabelsOnHeapData 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 3 with FeatureMatrixWithLabelsOnHeapData

use of org.apache.ignite.ml.dataset.primitive.FeatureMatrixWithLabelsOnHeapData in project ignite by apache.

the class ConvergenceChecker method isConverged.

/**
 * Checks convergency on dataset.
 *
 * @param envBuilder Learning environment builder.
 * @param currMdl Current model.
 * @return True if GDB is converged.
 */
public boolean isConverged(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K, V> datasetBuilder, ModelsComposition currMdl) {
    LearningEnvironment environment = envBuilder.buildForTrainer();
    environment.initDeployingContext(preprocessor);
    try (Dataset<EmptyContext, FeatureMatrixWithLabelsOnHeapData> dataset = datasetBuilder.build(envBuilder, new EmptyContextBuilder<>(), new FeatureMatrixWithLabelsOnHeapDataBuilder<>(preprocessor), environment)) {
        return isConverged(dataset, currMdl);
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
}

Example 4 with FeatureMatrixWithLabelsOnHeapData

use of org.apache.ignite.ml.dataset.primitive.FeatureMatrixWithLabelsOnHeapData in project ignite by apache.

the class MedianOfMedianConvergenceCheckerTest method testConvergenceChecking.

/**
 */
@Test
public void testConvergenceChecking() {
    data.put(666, VectorUtils.of(10, 11).labeled(100000.0));
    LocalDatasetBuilder<Integer, LabeledVector<Double>> datasetBuilder = new LocalDatasetBuilder<>(data, 1);
    ConvergenceChecker<Integer, LabeledVector<Double>> checker = createChecker(new MedianOfMedianConvergenceCheckerFactory(0.1), datasetBuilder);
    double error = checker.computeError(VectorUtils.of(1, 2), 4.0, notConvergedMdl);
    Assert.assertEquals(1.9, error, 0.01);
    LearningEnvironmentBuilder envBuilder = TestUtils.testEnvBuilder();
    Assert.assertFalse(checker.isConverged(envBuilder, datasetBuilder, notConvergedMdl));
    Assert.assertTrue(checker.isConverged(envBuilder, datasetBuilder, convergedMdl));
    try (LocalDataset<EmptyContext, FeatureMatrixWithLabelsOnHeapData> dataset = datasetBuilder.build(envBuilder, new EmptyContextBuilder<>(), new FeatureMatrixWithLabelsOnHeapDataBuilder<>(vectorizer), TestUtils.testEnvBuilder().buildForTrainer())) {
        double onDSError = checker.computeMeanErrorOnDataset(dataset, notConvergedMdl);
        Assert.assertEquals(1.6, onDSError, 0.01);
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
}