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

Example 11 with CounterfactualPrediction

use of org.kie.kogito.explainability.model.CounterfactualPrediction 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);
}

Example 12 with CounterfactualPrediction

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

the class CounterfactualExplainerTest method testConsumers.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2 })
void testConsumers(int seed) throws ExecutionException, InterruptedException, TimeoutException {
    Random random = new Random();
    random.setSeed(seed);
    final List<Output> goal = List.of(new Output("inside", Type.BOOLEAN, new Value(true), 0.0));
    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 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) seed);
    solverConfig.setEnvironmentMode(EnvironmentMode.REPRODUCIBLE);
    @SuppressWarnings("unchecked") final Consumer<CounterfactualResult> assertIntermediateCounterfactualNotNull = mock(Consumer.class);
    final CounterfactualConfig counterfactualConfig = new CounterfactualConfig().withSolverConfig(solverConfig).withGoalThreshold(0.01);
    final CounterfactualExplainer counterfactualExplainer = new CounterfactualExplainer(counterfactualConfig);
    PredictionInput input = new PredictionInput(features);
    final double center = 500.0;
    final double epsilon = 10.0;
    final PredictionProvider model = TestUtils.getSumThresholdModel(center, epsilon);
    PredictionOutput output = new PredictionOutput(goal);
    Prediction prediction = new CounterfactualPrediction(input, output, null, UUID.randomUUID(), null);
    final CounterfactualResult counterfactualResult = counterfactualExplainer.explainAsync(prediction, model, assertIntermediateCounterfactualNotNull).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    for (CounterfactualEntity entity : counterfactualResult.getEntities()) {
        logger.debug("Entity: {}", entity);
    }
    logger.debug("Outputs: {}", counterfactualResult.getOutput().get(0).getOutputs());
    // At least one intermediate result is generated
    verify(assertIntermediateCounterfactualNotNull, atLeast(1)).accept(any());
}

Example 13 with CounterfactualPrediction

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

the class CounterfactualExplainerTest method testNonEmptyInput.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2 })
void testNonEmptyInput(int seed) throws ExecutionException, InterruptedException, TimeoutException {
    Random random = new Random();
    random.setSeed(seed);
    final List<Output> goal = List.of(new Output("class", Type.NUMBER, new Value(10.0), 0.0d));
    List<Feature> features = new LinkedList<>();
    for (int i = 0; i < 4; i++) {
        features.add(FeatureFactory.newNumericalFeature("f-" + i, random.nextDouble(), NumericalFeatureDomain.create(0.0, 1000.0)));
    }
    final TerminationConfig terminationConfig = new TerminationConfig().withScoreCalculationCountLimit(10L);
    // for the purpose of this test, only a few steps are necessary
    final SolverConfig solverConfig = SolverConfigBuilder.builder().withTerminationConfig(terminationConfig).build();
    solverConfig.setRandomSeed((long) seed);
    solverConfig.setEnvironmentMode(EnvironmentMode.REPRODUCIBLE);
    final CounterfactualConfig counterfactualConfig = new CounterfactualConfig().withSolverConfig(solverConfig);
    final CounterfactualExplainer counterfactualExplainer = new CounterfactualExplainer(counterfactualConfig);
    PredictionProvider model = TestUtils.getSumSkipModel(0);
    PredictionInput input = new PredictionInput(features);
    PredictionOutput output = new PredictionOutput(goal);
    Prediction prediction = new CounterfactualPrediction(input, output, null, UUID.randomUUID(), null);
    final CounterfactualResult counterfactualResult = counterfactualExplainer.explainAsync(prediction, model).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    for (CounterfactualEntity entity : counterfactualResult.getEntities()) {
        logger.debug("Entity: {}", entity);
    }
    logger.debug("Outputs: {}", counterfactualResult.getOutput().get(0).getOutputs());
    assertNotNull(counterfactualResult);
    assertNotNull(counterfactualResult.getEntities());
}

Example 14 with CounterfactualPrediction

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

the class CounterfactualExplainerServiceHandlerTest method testGetPredictionWithFlatInputModel.

@Test
public void testGetPredictionWithFlatInputModel() {
    CounterfactualExplainabilityRequest request = new CounterfactualExplainabilityRequest(EXECUTION_ID, SERVICE_URL, MODEL_IDENTIFIER, COUNTERFACTUAL_ID, List.of(new NamedTypedValue("input1", new UnitValue("number", new IntNode(20)))), Collections.emptyList(), Collections.emptyList(), MAX_RUNNING_TIME_SECONDS);
    Prediction prediction = handler.getPrediction(request);
    assertTrue(prediction instanceof CounterfactualPrediction);
    CounterfactualPrediction counterfactualPrediction = (CounterfactualPrediction) prediction;
    assertEquals(1, counterfactualPrediction.getInput().getFeatures().size());
    Optional<Feature> oInput1 = counterfactualPrediction.getInput().getFeatures().stream().filter(f -> f.getName().equals("input1")).findFirst();
    assertTrue(oInput1.isPresent());
    Feature input1 = oInput1.get();
    assertEquals(Type.NUMBER, input1.getType());
    assertEquals(20, input1.getValue().asNumber());
    assertTrue(counterfactualPrediction.getInput().getFeatures().stream().allMatch(f -> f.getDomain().isEmpty()));
    assertTrue(counterfactualPrediction.getInput().getFeatures().stream().allMatch(Feature::isConstrained));
    assertTrue(counterfactualPrediction.getOutput().getOutputs().isEmpty());
    assertEquals(counterfactualPrediction.getMaxRunningTimeSeconds(), request.getMaxRunningTimeSeconds());
}

Example 15 with CounterfactualPrediction

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

the class CounterfactualExplainerServiceHandlerTest method testGetPredictionWithFlatOutputModel.

@Test
public void testGetPredictionWithFlatOutputModel() {
    CounterfactualExplainabilityRequest request = new CounterfactualExplainabilityRequest(EXECUTION_ID, SERVICE_URL, MODEL_IDENTIFIER, COUNTERFACTUAL_ID, Collections.emptyList(), List.of(new NamedTypedValue("output1", new UnitValue("number", new IntNode(20)))), Collections.emptyList(), MAX_RUNNING_TIME_SECONDS);
    Prediction prediction = handler.getPrediction(request);
    assertTrue(prediction instanceof CounterfactualPrediction);
    CounterfactualPrediction counterfactualPrediction = (CounterfactualPrediction) prediction;
    assertEquals(1, counterfactualPrediction.getOutput().getOutputs().size());
    Optional<Output> oOutput1 = counterfactualPrediction.getOutput().getOutputs().stream().filter(f -> f.getName().equals("output1")).findFirst();
    assertTrue(oOutput1.isPresent());
    Output output1 = oOutput1.get();
    assertEquals(Type.NUMBER, output1.getType());
    assertEquals(20, output1.getValue().asNumber());
    assertTrue(counterfactualPrediction.getInput().getFeatures().isEmpty());
    assertEquals(counterfactualPrediction.getMaxRunningTimeSeconds(), request.getMaxRunningTimeSeconds());
}