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

Example 6 with PredictionOutput

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

the class CounterfactualExplainerTest method mockExplainerInvocation.

@SuppressWarnings("unchecked")
CounterfactualResult mockExplainerInvocation(Consumer<CounterfactualResult> intermediateResultsConsumer, Long maxRunningTimeSeconds) throws ExecutionException, InterruptedException, TimeoutException {
    // Mock SolverManager and SolverJob to guarantee deterministic test behaviour
    SolverJob<CounterfactualSolution, UUID> solverJob = mock(SolverJob.class);
    CounterfactualSolution solution = mock(CounterfactualSolution.class);
    BendableBigDecimalScore score = BendableBigDecimalScore.zero(0, 0);
    when(solverManager.solveAndListen(any(), any(), any(), any())).thenReturn(solverJob);
    when(solverJob.getFinalBestSolution()).thenReturn(solution);
    when(solution.getScore()).thenReturn(score);
    when(solverManagerFactory.apply(any())).thenReturn(solverManager);
    // Setup Explainer
    final CounterfactualConfig counterfactualConfig = new CounterfactualConfig().withSolverManagerFactory(solverManagerFactory);
    final CounterfactualExplainer counterfactualExplainer = new CounterfactualExplainer(counterfactualConfig);
    // Setup mock model, what it does is not important
    Prediction prediction = new CounterfactualPrediction(new PredictionInput(Collections.emptyList()), new PredictionOutput(Collections.emptyList()), null, UUID.randomUUID(), maxRunningTimeSeconds);
    return counterfactualExplainer.explainAsync(prediction, (List<PredictionInput> inputs) -> CompletableFuture.completedFuture(Collections.emptyList()), intermediateResultsConsumer).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
}

Example 7 with PredictionOutput

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

the class CounterfactualExplainerTest method testIntermediateUniqueIds.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2 })
void testIntermediateUniqueIds(int seed) throws ExecutionException, InterruptedException, TimeoutException {
    Random random = new Random();
    random.setSeed(seed);
    final List<Output> goal = new ArrayList<>();
    List<Feature> features = List.of(FeatureFactory.newNumericalFeature("f-num1", 10.0, NumericalFeatureDomain.create(0, 20)));
    PredictionProvider model = TestUtils.getFeaturePassModel(0);
    final TerminationConfig terminationConfig = new TerminationConfig().withScoreCalculationCountLimit(100_000L);
    final SolverConfig solverConfig = SolverConfigBuilder.builder().withTerminationConfig(terminationConfig).build();
    solverConfig.setRandomSeed((long) seed);
    solverConfig.setEnvironmentMode(EnvironmentMode.REPRODUCIBLE);
    final List<UUID> intermediateIds = new ArrayList<>();
    final List<UUID> executionIds = new ArrayList<>();
    final Consumer<CounterfactualResult> captureIntermediateIds = counterfactual -> {
        intermediateIds.add(counterfactual.getSolutionId());
    };
    final Consumer<CounterfactualResult> captureExecutionIds = counterfactual -> {
        executionIds.add(counterfactual.getExecutionId());
    };
    final CounterfactualConfig counterfactualConfig = new CounterfactualConfig().withSolverConfig(solverConfig);
    solverConfig.withEasyScoreCalculatorClass(MockCounterFactualScoreCalculator.class);
    final CounterfactualExplainer counterfactualExplainer = new CounterfactualExplainer(counterfactualConfig);
    PredictionInput input = new PredictionInput(features);
    PredictionOutput output = new PredictionOutput(goal);
    final UUID executionId = UUID.randomUUID();
    Prediction prediction = new CounterfactualPrediction(input, output, null, executionId, null);
    final CounterfactualResult counterfactualResult = counterfactualExplainer.explainAsync(prediction, model, captureIntermediateIds.andThen(captureExecutionIds)).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    for (CounterfactualEntity entity : counterfactualResult.getEntities()) {
        logger.debug("Entity: {}", entity);
    }
    // all intermediate Ids must be distinct
    assertEquals((int) intermediateIds.stream().distinct().count(), intermediateIds.size());
    assertEquals(1, (int) executionIds.stream().distinct().count());
    assertEquals(executionIds.get(0), executionId);
}

Example 8 with PredictionOutput

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

the class CounterfactualExplainerTest method runCounterfactualSearch.

private CounterfactualResult runCounterfactualSearch(Long randomSeed, List<Output> goal, List<Feature> features, PredictionProvider model, double goalThresold) throws InterruptedException, ExecutionException, TimeoutException {
    final TerminationConfig terminationConfig = new TerminationConfig().withScoreCalculationCountLimit(steps);
    final SolverConfig solverConfig = SolverConfigBuilder.builder().withTerminationConfig(terminationConfig).build();
    solverConfig.setRandomSeed(randomSeed);
    solverConfig.setEnvironmentMode(EnvironmentMode.REPRODUCIBLE);
    final CounterfactualConfig counterfactualConfig = new CounterfactualConfig();
    counterfactualConfig.withSolverConfig(solverConfig).withGoalThreshold(goalThresold);
    final CounterfactualExplainer explainer = new CounterfactualExplainer(counterfactualConfig);
    final PredictionInput input = new PredictionInput(features);
    PredictionOutput output = new PredictionOutput(goal);
    Prediction prediction = new CounterfactualPrediction(input, output, null, UUID.randomUUID(), null);
    return explainer.explainAsync(prediction, model).get(predictionTimeOut, predictionTimeUnit);
}

Example 9 with PredictionOutput

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

the class CounterfactualScoreCalculatorTest method BooleanDistanceNull.

@Test
void BooleanDistanceNull() {
    // Null as a goal
    Feature predictionFeature = FeatureFactory.newBooleanFeature("x", true);
    Feature goalFeature = FeatureFactory.newBooleanFeature("y", null);
    Output predictionOutput = outputFromFeature(predictionFeature);
    Output goalOutput = outputFromFeature(goalFeature);
    double distance = CounterFactualScoreCalculator.outputDistance(predictionOutput, goalOutput);
    assertEquals(Type.BOOLEAN, goalOutput.getType());
    assertEquals(1.0, distance);
    // Null as a prediction
    predictionFeature = FeatureFactory.newBooleanFeature("x", null);
    goalFeature = FeatureFactory.newBooleanFeature("y", false);
    predictionOutput = outputFromFeature(predictionFeature);
    goalOutput = outputFromFeature(goalFeature);
    distance = CounterFactualScoreCalculator.outputDistance(predictionOutput, goalOutput);
    assertEquals(Type.BOOLEAN, predictionOutput.getType());
    assertEquals(1.0, distance);
    // Null as both prediction and goal
    predictionFeature = FeatureFactory.newBooleanFeature("x", null);
    goalFeature = FeatureFactory.newBooleanFeature("y", null);
    predictionOutput = outputFromFeature(predictionFeature);
    goalOutput = outputFromFeature(goalFeature);
    distance = CounterFactualScoreCalculator.outputDistance(predictionOutput, goalOutput);
    assertEquals(Type.BOOLEAN, predictionOutput.getType());
    assertEquals(0.0, distance);
}

Example 10 with PredictionOutput

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

the class CounterfactualScoreCalculatorTest method IntegerDistanceNull.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2, 3, 4 })
void IntegerDistanceNull(int seed) {
    final Random random = new Random(seed);
    final int value = random.nextInt(1000);
    // Null as a goal
    IllegalArgumentException exception = assertThrows(IllegalArgumentException.class, () -> {
        Feature predictionFeature = FeatureFactory.newNumericalFeature("x", value);
        Feature goalFeature = FeatureFactory.newNumericalFeature("x", null);
        Output predictionOutput = outputFromFeature(predictionFeature);
        Output goalOutput = outputFromFeature(goalFeature);
        CounterFactualScoreCalculator.outputDistance(predictionOutput, goalOutput);
    });
    assertEquals("Unsupported NaN or NULL for numeric feature 'x'", exception.getMessage());
    // Null as a prediction
    exception = assertThrows(IllegalArgumentException.class, () -> {
        Feature predictionFeature = FeatureFactory.newNumericalFeature("x", null);
        Feature goalFeature = FeatureFactory.newNumericalFeature("x", value);
        Output predictionOutput = outputFromFeature(predictionFeature);
        Output goalOutput = outputFromFeature(goalFeature);
        CounterFactualScoreCalculator.outputDistance(predictionOutput, goalOutput);
    });
    assertEquals("Unsupported NaN or NULL for numeric feature 'x'", exception.getMessage());
    // Null as both prediction and goal
    exception = assertThrows(IllegalArgumentException.class, () -> {
        Feature predictionFeature = FeatureFactory.newNumericalFeature("x", null);
        Feature goalFeature = FeatureFactory.newNumericalFeature("x", null);
        Output predictionOutput = outputFromFeature(predictionFeature);
        Output goalOutput = outputFromFeature(goalFeature);
        CounterFactualScoreCalculator.outputDistance(predictionOutput, goalOutput);
    });
    assertEquals("Unsupported NaN or NULL for numeric feature 'x'", exception.getMessage());
}