Examples with CounterfactualExplainer org.kie.kogito.explainability.local.counterfactual.CounterfactualExplainer used on opensource projects

Example 1 with CounterfactualExplainer

use of org.kie.kogito.explainability.local.counterfactual.CounterfactualExplainer in project kogito-apps by kiegroup.

the class CounterfactualExplainerProducerTest method produce.

@Test
void produce() {
    final ManagedExecutor executor = SmallRyeManagedExecutor.builder().build();
    CounterfactualExplainerProducer producer = new CounterfactualExplainerProducer(0.01, executor);
    CounterfactualExplainer counterfactualExplainer = producer.produce();
    assertNotNull(counterfactualExplainer);
    assertEquals(0.01, counterfactualExplainer.getCounterfactualConfig().getGoalThreshold());
    assertEquals(executor, counterfactualExplainer.getCounterfactualConfig().getExecutor());
}

Example 2 with CounterfactualExplainer

use of org.kie.kogito.explainability.local.counterfactual.CounterfactualExplainer in project kogito-apps by kiegroup.

the class LoanEligibilityDmnCounterfactualExplainerTest method testLoanEligibilityDMNExplanation.

@Test
void testLoanEligibilityDMNExplanation() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = getModel();
    final List<Output> goal = List.of(new Output("Is Enought?", Type.NUMBER, new Value(100), 0.0d), new Output("Eligibility", Type.TEXT, new Value("No"), 0.0d), new Output("Decide", Type.BOOLEAN, new Value(true), 0.0d));
    final TerminationConfig terminationConfig = new TerminationConfig().withScoreCalculationCountLimit(steps);
    final SolverConfig solverConfig = SolverConfigBuilder.builder().withTerminationConfig(terminationConfig).build();
    solverConfig.setRandomSeed(randomSeed);
    solverConfig.setEnvironmentMode(EnvironmentMode.REPRODUCIBLE);
    CounterfactualConfig config = new CounterfactualConfig();
    config.withSolverConfig(solverConfig);
    final CounterfactualExplainer explainer = new CounterfactualExplainer(config);
    PredictionInput input = getTestInput();
    PredictionOutput output = new PredictionOutput(goal);
    // test model
    List<PredictionOutput> predictionOutputs = model.predictAsync(List.of(input)).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    Prediction prediction = new CounterfactualPrediction(input, output, null, UUID.randomUUID(), null);
    CounterfactualResult counterfactualResult = explainer.explainAsync(prediction, model).get();
    List<Feature> cfFeatures = counterfactualResult.getEntities().stream().map(CounterfactualEntity::asFeature).collect(Collectors.toList());
    List<Feature> unflattened = CompositeFeatureUtils.unflattenFeatures(cfFeatures, input.getFeatures());
    List<PredictionOutput> outputs = model.predictAsync(List.of(new PredictionInput(unflattened))).get();
    assertTrue(counterfactualResult.isValid());
    final Output decideOutput = outputs.get(0).getOutputs().get(2);
    assertEquals("Decide", decideOutput.getName());
    assertTrue((Boolean) decideOutput.getValue().getUnderlyingObject());
}

Example 3 with CounterfactualExplainer

use of org.kie.kogito.explainability.local.counterfactual.CounterfactualExplainer in project kogito-apps by kiegroup.

the class ComplexEligibilityDmnCounterfactualExplainerTest method testDMNScoringFunction.

@Test
void testDMNScoringFunction() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = getModel();
    final List<Output> goal = generateGoal(true, true, 1.0);
    List<Feature> features = new LinkedList<>();
    features.add(FeatureFactory.newNumericalFeature("age", 40, NumericalFeatureDomain.create(18, 60)));
    features.add(FeatureFactory.newBooleanFeature("hasReferral", true));
    features.add(FeatureFactory.newNumericalFeature("monthlySalary", 500, NumericalFeatureDomain.create(10, 100_000)));
    final TerminationConfig terminationConfig = new TerminationConfig().withScoreCalculationCountLimit(10_000L);
    // for the purpose of this test, only a few steps are necessary
    final SolverConfig solverConfig = SolverConfigBuilder.builder().withTerminationConfig(terminationConfig).build();
    solverConfig.setRandomSeed((long) 23);
    solverConfig.setEnvironmentMode(EnvironmentMode.REPRODUCIBLE);
    final CounterfactualConfig counterfactualConfig = new CounterfactualConfig().withSolverConfig(solverConfig).withGoalThreshold(0.01);
    final CounterfactualExplainer counterfactualExplainer = new CounterfactualExplainer(counterfactualConfig);
    PredictionInput input = new PredictionInput(features);
    PredictionOutput output = new PredictionOutput(goal);
    Prediction prediction = new CounterfactualPrediction(input, output, null, UUID.randomUUID(), 60L);
    final CounterfactualResult counterfactualResult = counterfactualExplainer.explainAsync(prediction, model).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    List<Output> cfOutputs = counterfactualResult.getOutput().get(0).getOutputs();
    assertTrue(counterfactualResult.isValid());
    assertEquals("inputsAreValid", cfOutputs.get(0).getName());
    assertTrue((Boolean) cfOutputs.get(0).getValue().getUnderlyingObject());
    assertEquals("canRequestLoan", cfOutputs.get(1).getName());
    assertTrue((Boolean) cfOutputs.get(1).getValue().getUnderlyingObject());
    assertEquals("my-scoring-function", cfOutputs.get(2).getName());
    assertEquals(1.0, ((BigDecimal) cfOutputs.get(2).getValue().getUnderlyingObject()).doubleValue(), 0.01);
    List<CounterfactualEntity> entities = counterfactualResult.getEntities();
    assertEquals("age", entities.get(0).asFeature().getName());
    assertEquals(18, entities.get(0).asFeature().getValue().asNumber());
    assertEquals("hasReferral", entities.get(1).asFeature().getName());
    assertTrue((Boolean) entities.get(1).asFeature().getValue().getUnderlyingObject());
    assertEquals("monthlySalary", entities.get(2).asFeature().getName());
    final double monthlySalary = entities.get(2).asFeature().getValue().asNumber();
    assertEquals(7900, monthlySalary, 10);
    // since the scoring function is ((0.6 * ((42 - age + 18)/42)) + (0.4 * (monthlySalary/8000)))
    // for a result of 1.0 the relation must be age = (7*monthlySalary)/2000 - 10
    assertEquals(18, (7 * monthlySalary) / 2000.0 - 10.0, 0.5);
}

Example 4 with CounterfactualExplainer

use of org.kie.kogito.explainability.local.counterfactual.CounterfactualExplainer in project kogito-apps by kiegroup.

the class ComplexEligibilityDmnCounterfactualExplainerTest method testDMNInvalidCounterfactualExplanation.

@Test
void testDMNInvalidCounterfactualExplanation() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = getModel();
    final List<Output> goal = generateGoal(true, true, 0.6);
    List<Feature> features = new LinkedList<>();
    // DMN model does not allow loans for age >= 60, so no CF will be possible
    features.add(FeatureFactory.newNumericalFeature("age", 61));
    features.add(FeatureFactory.newBooleanFeature("hasReferral", true));
    features.add(FeatureFactory.newNumericalFeature("monthlySalary", 500, NumericalFeatureDomain.create(10, 10_000)));
    final TerminationConfig terminationConfig = new TerminationConfig().withScoreCalculationCountLimit(10_000L);
    // for the purpose of this test, only a few steps are necessary
    final SolverConfig solverConfig = SolverConfigBuilder.builder().withTerminationConfig(terminationConfig).build();
    solverConfig.setRandomSeed((long) 23);
    solverConfig.setEnvironmentMode(EnvironmentMode.REPRODUCIBLE);
    final CounterfactualConfig counterfactualConfig = new CounterfactualConfig().withSolverConfig(solverConfig).withGoalThreshold(0.01);
    final CounterfactualExplainer counterfactualExplainer = new CounterfactualExplainer(counterfactualConfig);
    PredictionInput input = new PredictionInput(features);
    PredictionOutput output = new PredictionOutput(goal);
    Prediction prediction = new CounterfactualPrediction(input, output, null, UUID.randomUUID(), 60L);
    final CounterfactualResult counterfactualResult = counterfactualExplainer.explainAsync(prediction, model).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    assertFalse(counterfactualResult.isValid());
}

Example 5 with CounterfactualExplainer

use of org.kie.kogito.explainability.local.counterfactual.CounterfactualExplainer in project kogito-apps by kiegroup.

the class ComplexEligibilityDmnCounterfactualExplainerTest method testDMNValidCounterfactualExplanation.

@Test
void testDMNValidCounterfactualExplanation() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = getModel();
    final List<Output> goal = generateGoal(true, true, 0.6);
    List<Feature> features = new LinkedList<>();
    features.add(FeatureFactory.newNumericalFeature("age", 40));
    features.add(FeatureFactory.newBooleanFeature("hasReferral", true));
    features.add(FeatureFactory.newNumericalFeature("monthlySalary", 500, NumericalFeatureDomain.create(10, 10_000)));
    final TerminationConfig terminationConfig = new TerminationConfig().withScoreCalculationCountLimit(10_000L);
    // for the purpose of this test, only a few steps are necessary
    final SolverConfig solverConfig = SolverConfigBuilder.builder().withTerminationConfig(terminationConfig).build();
    solverConfig.setRandomSeed((long) 23);
    solverConfig.setEnvironmentMode(EnvironmentMode.REPRODUCIBLE);
    final CounterfactualConfig counterfactualConfig = new CounterfactualConfig().withSolverConfig(solverConfig).withGoalThreshold(0.01);
    final CounterfactualExplainer counterfactualExplainer = new CounterfactualExplainer(counterfactualConfig);
    PredictionInput input = new PredictionInput(features);
    PredictionOutput output = new PredictionOutput(goal);
    Prediction prediction = new CounterfactualPrediction(input, output, null, UUID.randomUUID(), 60L);
    final CounterfactualResult counterfactualResult = counterfactualExplainer.explainAsync(prediction, model).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    List<Output> cfOutputs = counterfactualResult.getOutput().get(0).getOutputs();
    assertTrue(counterfactualResult.isValid());
    assertEquals("inputsAreValid", cfOutputs.get(0).getName());
    assertTrue((Boolean) cfOutputs.get(0).getValue().getUnderlyingObject());
    assertEquals("canRequestLoan", cfOutputs.get(1).getName());
    assertTrue((Boolean) cfOutputs.get(1).getValue().getUnderlyingObject());
    assertEquals("my-scoring-function", cfOutputs.get(2).getName());
    assertEquals(0.6, ((BigDecimal) cfOutputs.get(2).getValue().getUnderlyingObject()).doubleValue(), 0.05);
    List<CounterfactualEntity> entities = counterfactualResult.getEntities();
    assertEquals("age", entities.get(0).asFeature().getName());
    assertEquals(40, entities.get(0).asFeature().getValue().asNumber());
    assertEquals("hasReferral", entities.get(1).asFeature().getName());
    assertTrue((Boolean) entities.get(1).asFeature().getValue().getUnderlyingObject());
    assertEquals("monthlySalary", entities.get(2).asFeature().getName());
    assertTrue(entities.get(2).asFeature().getValue().asNumber() > 6000);
}