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

Example 81 with Output

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

the class CounterfactualExplainerTest method testCounterfactualMatchThreshold.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2 })
void testCounterfactualMatchThreshold(int seed) throws ExecutionException, InterruptedException, TimeoutException {
    Random random = new Random();
    random.setSeed(seed);
    final double scoreThreshold = 0.9;
    final List<Output> goal = List.of(new Output("inside", Type.BOOLEAN, new Value(true), scoreThreshold));
    List<Feature> features = new LinkedList<>();
    features.add(FeatureFactory.newNumericalFeature("f-num1", 100.0, NumericalFeatureDomain.create(0.0, 1000.0)));
    features.add(FeatureFactory.newNumericalFeature("f-num2", 100.0, NumericalFeatureDomain.create(0.0, 1000.0)));
    features.add(FeatureFactory.newNumericalFeature("f-num3", 100.0, NumericalFeatureDomain.create(0.0, 1000.0)));
    features.add(FeatureFactory.newNumericalFeature("f-num4", 100.0, NumericalFeatureDomain.create(0.0, 1000.0)));
    final double center = 500.0;
    final double epsilon = 10.0;
    final PredictionProvider model = TestUtils.getSumThresholdModel(center, epsilon);
    final CounterfactualResult result = runCounterfactualSearch((long) seed, goal, features, model, DEFAULT_GOAL_THRESHOLD);
    final List<CounterfactualEntity> counterfactualEntities = result.getEntities();
    double totalSum = 0;
    for (CounterfactualEntity entity : counterfactualEntities) {
        totalSum += entity.asFeature().getValue().asNumber();
        logger.debug("Entity: {}", entity);
    }
    assertTrue(totalSum <= center + epsilon);
    assertTrue(totalSum >= center - epsilon);
    final List<Feature> cfFeatures = counterfactualEntities.stream().map(CounterfactualEntity::asFeature).collect(Collectors.toList());
    final PredictionInput cfInput = new PredictionInput(cfFeatures);
    final PredictionOutput cfOutput = model.predictAsync(List.of(cfInput)).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit()).get(0);
    final double predictionScore = cfOutput.getOutputs().get(0).getScore();
    logger.debug("Prediction score: {}", predictionScore);
    assertTrue(predictionScore >= scoreThreshold);
    assertTrue(result.isValid());
}

Example 82 with Output

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

the class CounterfactualExplainerTest method testCounterfactualMatchNoThreshold.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2 })
void testCounterfactualMatchNoThreshold(int seed) throws ExecutionException, InterruptedException, TimeoutException {
    Random random = new Random();
    random.setSeed(seed);
    final double scoreThreshold = 0.0;
    final List<Output> goal = List.of(new Output("inside", Type.BOOLEAN, new Value(true), scoreThreshold));
    List<Feature> features = new LinkedList<>();
    features.add(FeatureFactory.newNumericalFeature("f-num1", 100.0, NumericalFeatureDomain.create(0.0, 1000.0)));
    features.add(FeatureFactory.newNumericalFeature("f-num2", 100.0, NumericalFeatureDomain.create(0.0, 1000.0)));
    features.add(FeatureFactory.newNumericalFeature("f-num3", 100.0, NumericalFeatureDomain.create(0.0, 1000.0)));
    features.add(FeatureFactory.newNumericalFeature("f-num4", 100.0, NumericalFeatureDomain.create(0.0, 1000.0)));
    final double center = 500.0;
    final double epsilon = 10.0;
    final PredictionProvider model = TestUtils.getSumThresholdModel(center, epsilon);
    final CounterfactualResult result = runCounterfactualSearch((long) seed, goal, features, model, DEFAULT_GOAL_THRESHOLD);
    final List<CounterfactualEntity> counterfactualEntities = result.getEntities();
    double totalSum = 0;
    for (CounterfactualEntity entity : counterfactualEntities) {
        totalSum += entity.asFeature().getValue().asNumber();
        logger.debug("Entity: {}", entity);
    }
    assertTrue(totalSum <= center + epsilon);
    assertTrue(totalSum >= center - epsilon);
    final List<Feature> cfFeatures = counterfactualEntities.stream().map(CounterfactualEntity::asFeature).collect(Collectors.toList());
    final PredictionInput cfInput = new PredictionInput(cfFeatures);
    final PredictionOutput cfOutput = model.predictAsync(List.of(cfInput)).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit()).get(0);
    final double predictionScore = cfOutput.getOutputs().get(0).getScore();
    logger.debug("Prediction score: {}", predictionScore);
    assertTrue(predictionScore < 0.5);
    assertTrue(result.isValid());
}

Example 83 with Output

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

the class CounterfactualExplainerTest method testNoCounterfactualPossible.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2 })
void testNoCounterfactualPossible(long seed) throws ExecutionException, InterruptedException, TimeoutException {
    Random random = new Random();
    final PerturbationContext perturbationContext = new PerturbationContext(seed, random, 4);
    final List<Output> goal = List.of(new Output("inside", Type.BOOLEAN, new Value(true), 0.0));
    List<Feature> features = new LinkedList<>();
    List<FeatureDomain> featureBoundaries = new LinkedList<>();
    List<Boolean> constraints = new LinkedList<>();
    features.add(FeatureFactory.newNumericalFeature("f-num1", 1.0));
    constraints.add(true);
    featureBoundaries.add(EmptyFeatureDomain.create());
    features.add(FeatureFactory.newNumericalFeature("f-num2", 1.0));
    constraints.add(false);
    featureBoundaries.add(NumericalFeatureDomain.create(0.0, 2.0));
    features.add(FeatureFactory.newNumericalFeature("f-num3", 1.0));
    constraints.add(false);
    featureBoundaries.add(NumericalFeatureDomain.create(0.0, 2.0));
    features.add(FeatureFactory.newNumericalFeature("f-num4", 1.0));
    constraints.add(true);
    featureBoundaries.add(EmptyFeatureDomain.create());
    final DataDomain dataDomain = new DataDomain(featureBoundaries);
    final double center = 500.0;
    final double epsilon = 1.0;
    List<Feature> perturbedFeatures = DataUtils.perturbFeatures(features, perturbationContext);
    final CounterfactualResult result = runCounterfactualSearch((long) seed, goal, perturbedFeatures, TestUtils.getSumThresholdModel(center, epsilon), DEFAULT_GOAL_THRESHOLD);
    assertFalse(result.isValid());
}

Example 84 with Output

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

the class CounterfactualScoreCalculatorTest method binaryDistanceDifferentValue.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2, 3, 4 })
void binaryDistanceDifferentValue(int seed) {
    final Random random = new Random(seed);
    Feature x = FeatureFactory.newBinaryFeature("x", ByteBuffer.wrap("foo".getBytes()));
    Feature y = FeatureFactory.newBinaryFeature("y", ByteBuffer.wrap("bar".getBytes()));
    Output ox = outputFromFeature(x);
    Output oy = outputFromFeature(y);
    double distance = CounterFactualScoreCalculator.outputDistance(ox, oy);
    assertEquals(Type.BINARY, ox.getType());
    assertEquals(Type.BINARY, oy.getType());
    assertEquals(1.0, distance);
    // Use a random threshold, mustn't make a difference
    distance = CounterFactualScoreCalculator.outputDistance(ox, oy, random.nextDouble());
    assertEquals(1.0, distance);
}

Example 85 with Output

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

the class CounterfactualScoreCalculatorTest method testNullBooleanInput.

/**
 * Null values for input Boolean features should be accepted as valid
 */
@Test
void testNullBooleanInput() 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.newBooleanFeature("f-2", null));
    featureDomains.add(EmptyFeatureDomain.create());
    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.BOOLEAN, new Value(null), 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());
}