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

Example 6 with PredictionInput

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

the class CounterfactualExplainerTest method testFinalUniqueIds.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2 })
void testFinalUniqueIds(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 should be unique
    assertEquals((int) intermediateIds.stream().distinct().count(), intermediateIds.size());
    // There should be at least one intermediate id
    assertTrue(intermediateIds.size() > 0);
    // There should be at least one execution id
    assertTrue(executionIds.size() > 0);
    // We should have the same number of execution ids as intermediate ids (captured from intermediate results)
    assertEquals(executionIds.size(), intermediateIds.size());
    // All execution ids should be the same
    assertEquals(1, (int) executionIds.stream().distinct().count());
    // The last intermediate id must be different from the final result id
    assertNotEquals(intermediateIds.get(intermediateIds.size() - 1), counterfactualResult.getSolutionId());
    // Captured execution ids should be the same as the one provided
    assertEquals(executionIds.get(0), executionId);
}

Example 7 with PredictionInput

use of org.kie.kogito.explainability.model.PredictionInput 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 8 with PredictionInput

use of org.kie.kogito.explainability.model.PredictionInput 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 9 with PredictionInput

use of org.kie.kogito.explainability.model.PredictionInput 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 10 with PredictionInput

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

the class CounterfactualScoreCalculatorTest method testNullIntegerInput.

/**
 * Null values for input Integer features should not be accepted as valid
 */
@Test
void testNullIntegerInput() throws ExecutionException, InterruptedException {
    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, 10));
    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());
}