Examples with AdaptableDatasetModel org.apache.ignite.ml.trainers.AdaptableDatasetModel used on opensource projects

Example 1 with AdaptableDatasetModel

use of org.apache.ignite.ml.trainers.AdaptableDatasetModel in project ignite by apache.

the class StackingTest method testSimpleStack.

/**
 * Tests simple stack training.
 */
@Test
public void testSimpleStack() {
    StackedDatasetTrainer<Vector, Vector, Double, LinearRegressionModel, Double> trainer = new StackedDatasetTrainer<>();
    UpdatesStrategy<SmoothParametrized, SimpleGDParameterUpdate> updatesStgy = new UpdatesStrategy<>(new SimpleGDUpdateCalculator(0.2), SimpleGDParameterUpdate.SUM_LOCAL, SimpleGDParameterUpdate.AVG);
    MLPArchitecture arch = new MLPArchitecture(2).withAddedLayer(10, true, Activators.RELU).withAddedLayer(1, false, Activators.SIGMOID);
    MLPTrainer<SimpleGDParameterUpdate> trainer1 = new MLPTrainer<>(arch, LossFunctions.MSE, updatesStgy, 3000, 10, 50, 123L);
    // Convert model trainer to produce Vector -> Vector model
    DatasetTrainer<AdaptableDatasetModel<Vector, Vector, Matrix, Matrix, MultilayerPerceptron>, Double> mlpTrainer = AdaptableDatasetTrainer.of(trainer1).beforeTrainedModel((Vector v) -> new DenseMatrix(v.asArray(), 1)).afterTrainedModel((Matrix mtx) -> mtx.getRow(0)).withConvertedLabels(VectorUtils::num2Arr);
    final double factor = 3;
    StackedModel<Vector, Vector, Double, LinearRegressionModel> mdl = trainer.withAggregatorTrainer(new LinearRegressionLSQRTrainer().withConvertedLabels(x -> x * factor)).addTrainer(mlpTrainer).withAggregatorInputMerger(VectorUtils::concat).withSubmodelOutput2VectorConverter(IgniteFunction.identity()).withVector2SubmodelInputConverter(IgniteFunction.identity()).withOriginalFeaturesKept(IgniteFunction.identity()).withEnvironmentBuilder(TestUtils.testEnvBuilder()).fit(getCacheMock(xor), parts, new DoubleArrayVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.LAST));
    assertEquals(0.0 * factor, mdl.predict(VectorUtils.of(0.0, 0.0)), 0.3);
    assertEquals(1.0 * factor, mdl.predict(VectorUtils.of(0.0, 1.0)), 0.3);
    assertEquals(1.0 * factor, mdl.predict(VectorUtils.of(1.0, 0.0)), 0.3);
    assertEquals(0.0 * factor, mdl.predict(VectorUtils.of(1.0, 1.0)), 0.3);
}

Example 2 with AdaptableDatasetModel

use of org.apache.ignite.ml.trainers.AdaptableDatasetModel in project ignite by apache.

the class BaggingTest method testNaiveBaggingLogRegression.

/**
 * Test that bagged log regression makes correct predictions.
 */
@Test
public void testNaiveBaggingLogRegression() {
    Map<Integer, double[]> cacheMock = getCacheMock(twoLinearlySeparableClasses);
    DatasetTrainer<LogisticRegressionModel, Double> trainer = new LogisticRegressionSGDTrainer().withUpdatesStgy(new UpdatesStrategy<>(new SimpleGDUpdateCalculator(0.2), SimpleGDParameterUpdate.SUM_LOCAL, SimpleGDParameterUpdate.AVG)).withMaxIterations(30000).withLocIterations(100).withBatchSize(10).withSeed(123L);
    BaggedTrainer<Double> baggedTrainer = TrainerTransformers.makeBagged(trainer, 7, 0.7, 2, 2, new OnMajorityPredictionsAggregator()).withEnvironmentBuilder(TestUtils.testEnvBuilder());
    BaggedModel mdl = baggedTrainer.fit(cacheMock, parts, new DoubleArrayVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST));
    Vector weights = ((LogisticRegressionModel) ((AdaptableDatasetModel) ((ModelsParallelComposition) ((AdaptableDatasetModel) mdl.model()).innerModel()).submodels().get(0)).innerModel()).weights();
    TestUtils.assertEquals(firstMdlWeights.get(parts), weights, 0.0);
    TestUtils.assertEquals(0, mdl.predict(VectorUtils.of(100, 10)), PRECISION);
    TestUtils.assertEquals(1, mdl.predict(VectorUtils.of(10, 100)), PRECISION);
}