Examples with Feature org.kie.kogito.explainability.model.Feature used on opensource projects

Example 76 with Feature

use of org.kie.kogito.explainability.model.Feature in project kogito-apps by kiegroup.

the class CounterfactualScoreCalculatorTest method TextDistanceSameValue.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2, 3, 4 })
void TextDistanceSameValue(int seed) {
    final String value = UUID.randomUUID().toString();
    Feature x = FeatureFactory.newTextFeature("x", value);
    Feature y = FeatureFactory.newTextFeature("y", value);
    Output ox = outputFromFeature(x);
    Output oy = outputFromFeature(y);
    final double distance = CounterFactualScoreCalculator.outputDistance(ox, oy);
    assertEquals(Type.TEXT, ox.getType());
    assertEquals(0.0, distance);
}

Example 77 with Feature

use of org.kie.kogito.explainability.model.Feature in project kogito-apps by kiegroup.

the class CounterfactualScoreCalculatorTest method testGoalSizeMatch.

/**
 * If the goal and the model's output is the same, the distances should all be zero.
 */
@Test
void testGoalSizeMatch() throws ExecutionException, InterruptedException {
    final CounterFactualScoreCalculator scoreCalculator = new CounterFactualScoreCalculator();
    PredictionProvider model = TestUtils.getFeatureSkipModel(0);
    List<Feature> features = new ArrayList<>();
    List<FeatureDomain> featureDomains = new ArrayList<>();
    List<Boolean> constraints = new ArrayList<>();
    // f-1
    features.add(FeatureFactory.newNumericalFeature("f-1", 1.0));
    featureDomains.add(NumericalFeatureDomain.create(0.0, 10.0));
    constraints.add(false);
    // f-2
    features.add(FeatureFactory.newNumericalFeature("f-2", 2.0));
    featureDomains.add(NumericalFeatureDomain.create(0.0, 10.0));
    constraints.add(false);
    // f-3
    features.add(FeatureFactory.newBooleanFeature("f-3", true));
    featureDomains.add(EmptyFeatureDomain.create());
    constraints.add(false);
    PredictionInput input = new PredictionInput(features);
    PredictionFeatureDomain domains = new PredictionFeatureDomain(featureDomains);
    List<CounterfactualEntity> entities = CounterfactualEntityFactory.createEntities(input);
    List<Output> goal = new ArrayList<>();
    goal.add(new Output("f-2", Type.NUMBER, new Value(2.0), 0.0));
    goal.add(new Output("f-3", Type.BOOLEAN, new Value(true), 0.0));
    final CounterfactualSolution solution = new CounterfactualSolution(entities, features, model, goal, UUID.randomUUID(), UUID.randomUUID(), 0.0);
    BendableBigDecimalScore score = scoreCalculator.calculateScore(solution);
    List<PredictionOutput> predictionOutputs = model.predictAsync(List.of(input)).get();
    assertTrue(score.isFeasible());
    assertEquals(2, goal.size());
    // A single prediction is expected
    assertEquals(1, predictionOutputs.size());
    // Single prediction with two features
    assertEquals(2, predictionOutputs.get(0).getOutputs().size());
    assertEquals(0, score.getHardScore(0).compareTo(BigDecimal.ZERO));
    assertEquals(0, score.getHardScore(1).compareTo(BigDecimal.ZERO));
    assertEquals(0, score.getHardScore(2).compareTo(BigDecimal.ZERO));
    assertEquals(0, score.getSoftScore(0).compareTo(BigDecimal.ZERO));
    assertEquals(0, score.getSoftScore(1).compareTo(BigDecimal.ZERO));
    assertEquals(3, score.getHardLevelsSize());
    assertEquals(2, score.getSoftLevelsSize());
}

Example 78 with Feature

use of org.kie.kogito.explainability.model.Feature in project kogito-apps by kiegroup.

the class CounterfactualScoreCalculatorTest method DoubleDistanceSameValue.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2, 3, 4 })
void DoubleDistanceSameValue(int seed) {
    final Random random = new Random(seed);
    final double value = random.nextDouble();
    Feature x = FeatureFactory.newNumericalFeature("x", value);
    Feature y = FeatureFactory.newNumericalFeature("y", value);
    Output ox = outputFromFeature(x);
    Output oy = outputFromFeature(y);
    // Use a random threshold, mustn't make a difference
    final double distance = CounterFactualScoreCalculator.outputDistance(ox, oy, random.nextDouble());
    assertEquals(Type.NUMBER, ox.getType());
    assertEquals(0.0, distance);
}

Example 79 with Feature

use of org.kie.kogito.explainability.model.Feature in project kogito-apps by kiegroup.

the class CounterfactualScoreCalculatorTest method unsupportedFeatureType.

@Test
void unsupportedFeatureType() {
    Feature x = FeatureFactory.newVectorFeature("x", 1, 2, 3, 4);
    Feature y = FeatureFactory.newVectorFeature("y", 5, 6, 7, 8);
    Output ox = outputFromFeature(x);
    Output oy = outputFromFeature(y);
    IllegalArgumentException exception = assertThrows(IllegalArgumentException.class, () -> {
        CounterFactualScoreCalculator.outputDistance(ox, oy);
    });
    assertEquals("Feature 'x' has unsupported type 'vector'", exception.getMessage());
}

Example 80 with Feature

use of org.kie.kogito.explainability.model.Feature in project kogito-apps by kiegroup.

the class CompositeEntityTest method testBasicSerDe.

@Test
void testBasicSerDe() {
    final List<Feature> features = new ArrayList<>();
    final Feature compositeFeature = generateCompositeFeature();
    features.add(compositeFeature);
    final List<Feature> flattened = CompositeFeatureUtils.flattenFeatures(features);
    final List<Feature> delinearised = CompositeFeatureUtils.unflattenFeatures(flattened, features);
    assertEquals(features, delinearised);
}