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

Example 6 with PredictionFeatureDomain

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

the class CounterfactualScoreCalculatorTest method testPrimarySoftScore.

/**
 * Test precision errors for primary soft score.
 * When the primary soft score is calculated between features with the same numerical
 * value a similarity of 1 is expected. For a large number of features, due to floating point errors this distance may be
 * in some cases slightly larger than 1, which will cause the distance (Math.sqrt(1.0-similarity)) to cause an exception.
 * The score calculation method should not let this should not occur.
 */
@ParameterizedTest
@ValueSource(ints = { 0, 1, 2, 3, 4 })
void testPrimarySoftScore(int seed) {
    final Random random = new Random(seed);
    final List<Feature> features = new ArrayList<>();
    final List<FeatureDomain> featureDomains = new ArrayList<>();
    final List<Boolean> constraints = new ArrayList<>();
    final int nFeatures = 1000;
    // Create a large number of identical features
    for (int n = 0; n < nFeatures; n++) {
        features.add(FeatureFactory.newNumericalFeature("f-" + n, random.nextDouble() * 1e-100));
        featureDomains.add(NumericalFeatureDomain.create(0.0, 10.0));
        constraints.add(false);
    }
    final PredictionInput input = new PredictionInput(features);
    final PredictionFeatureDomain domain = new PredictionFeatureDomain(featureDomains);
    final List<CounterfactualEntity> entities = CounterfactualEntityFactory.createEntities(input);
    // Create score calculator and model
    final CounterFactualScoreCalculator scoreCalculator = new CounterFactualScoreCalculator();
    PredictionProvider model = TestUtils.getFeatureSkipModel(0);
    // Create goal
    final List<Output> goal = new ArrayList<>();
    for (int n = 1; n < nFeatures; n++) {
        goal.add(new Output("f-" + n, Type.NUMBER, features.get(n).getValue(), 1.0));
    }
    final CounterfactualSolution solution = new CounterfactualSolution(entities, features, model, goal, UUID.randomUUID(), UUID.randomUUID(), 0.0);
    final BendableBigDecimalScore score = scoreCalculator.calculateScore(solution);
    assertEquals(0.0, score.getSoftScore(0).doubleValue(), 1e-5);
}

Example 7 with PredictionFeatureDomain

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

the class CounterfactualScoreCalculatorTest method testNullDoubleInput.

/**
 * Null values for input Double features should not be accepted as valid
 */
@Test
void testNullDoubleInput() {
    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", null));
    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);
    IllegalArgumentException exception = assertThrows(IllegalArgumentException.class, () -> {
        CounterfactualEntityFactory.createEntities(input);
    });
    assertEquals("Null numeric features are not supported in counterfactuals", exception.getMessage());
}