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

Example 21 with Output

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

the class CounterfactualScoreCalculatorTest method objectDistanceSameValue.

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

Example 22 with Output

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

the class CounterfactualScoreCalculatorTest method durationDistanceDifferentValue.

@Test
void durationDistanceDifferentValue() {
    final double SECONDS = 120L;
    Feature x = FeatureFactory.newDurationFeature("x", Duration.ZERO);
    Feature y = FeatureFactory.newDurationFeature("y", Duration.ofSeconds((long) SECONDS));
    Output ox = outputFromFeature(x);
    Output oy = outputFromFeature(y);
    double distance = CounterFactualScoreCalculator.outputDistance(ox, oy);
    assertEquals(Type.DURATION, ox.getType());
    assertEquals(Type.DURATION, oy.getType());
    assertEquals(SECONDS, distance);
    x = FeatureFactory.newDurationFeature("x", Duration.ofSeconds((long) SECONDS));
    y = FeatureFactory.newDurationFeature("y", Duration.ofDays(1L));
    ox = outputFromFeature(x);
    oy = outputFromFeature(y);
    distance = CounterFactualScoreCalculator.outputDistance(ox, oy);
    assertEquals(0.9986, distance, 0.01);
    x = FeatureFactory.newDurationFeature("x", Duration.ofDays(2L));
    y = FeatureFactory.newDurationFeature("y", Duration.ofDays(1L));
    ox = outputFromFeature(x);
    oy = outputFromFeature(y);
    distance = CounterFactualScoreCalculator.outputDistance(ox, oy);
    System.out.println(distance);
    assertEquals(0.5, distance, 1e-4);
}

Example 23 with Output

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

the class CounterfactualScoreCalculatorTest method CategoricalDistanceSameValue.

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

Example 24 with Output

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

the class CounterfactualScoreCalculatorTest method testGoalSizeSmaller.

/**
 * Using a smaller number of features in the goals (1) than the model's output (2) should
 * throw an {@link IllegalArgumentException} with the appropriate message.
 */
@Test
void testGoalSizeSmaller() 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));
    List<PredictionOutput> predictionOutputs = model.predictAsync(List.of(input)).get();
    assertEquals(1, goal.size());
    // A single prediction is expected
    assertEquals(1, predictionOutputs.size());
    // Single prediction with two features
    assertEquals(2, predictionOutputs.get(0).getOutputs().size());
    final CounterfactualSolution solution = new CounterfactualSolution(entities, features, model, goal, UUID.randomUUID(), UUID.randomUUID(), 0.0);
    IllegalArgumentException exception = assertThrows(IllegalArgumentException.class, () -> {
        scoreCalculator.calculateScore(solution);
    });
    assertEquals("Prediction size must be equal to goal size", exception.getMessage());
}

Example 25 with Output

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

the class CounterfactualScoreCalculatorTest method BooleanDistanceDifferentValueThreshold.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2, 3, 4 })
void BooleanDistanceDifferentValueThreshold(int seed) {
    final Random random = new Random(seed);
    boolean value = random.nextBoolean();
    Feature x = FeatureFactory.newBooleanFeature("x", value);
    Feature y = FeatureFactory.newBooleanFeature("y", !value);
    Output ox = outputFromFeature(x);
    Output oy = outputFromFeature(y);
    double distance = CounterFactualScoreCalculator.outputDistance(ox, oy, 0.25);
    assertEquals(Type.BOOLEAN, ox.getType());
    assertEquals(Type.BOOLEAN, oy.getType());
    assertEquals(1.0, distance);
}