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

Example 61 with PredictionOutput

use of org.kie.kogito.explainability.model.PredictionOutput 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 62 with PredictionOutput

use of org.kie.kogito.explainability.model.PredictionOutput 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 63 with PredictionOutput

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

the class DecisionModelWrapper method predictAsync.

@Override
public CompletableFuture<List<PredictionOutput>> predictAsync(List<PredictionInput> inputs) {
    List<PredictionOutput> predictionOutputs = new LinkedList<>();
    for (PredictionInput input : inputs) {
        Map<String, Object> contextVariables = toMap(input.getFeatures());
        final DMNContext context = decisionModel.newContext(contextVariables);
        DMNResult dmnResult = decisionModel.evaluateAll(context);
        List<Output> outputs = new LinkedList<>();
        for (DMNDecisionResult decisionResult : dmnResult.getDecisionResults()) {
            String decisionName = decisionResult.getDecisionName();
            if (!skippedDecisions.contains(decisionName)) {
                Object result = decisionResult.getResult();
                Value value = new Value(result);
                Type type;
                if (result == null) {
                    type = Type.TEXT;
                } else {
                    if (result instanceof Boolean) {
                        type = Type.BOOLEAN;
                    } else if (result instanceof String) {
                        type = Type.TEXT;
                    } else {
                        type = Type.NUMBER;
                    }
                }
                Output output = new Output(decisionName, type, value, 1d);
                outputs.add(output);
            }
        }
        PredictionOutput predictionOutput = new PredictionOutput(outputs);
        predictionOutputs.add(predictionOutput);
    }
    return completedFuture(predictionOutputs);
}

Example 64 with PredictionOutput

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

the class LimeConfigOptimizerTest method assertConfigOptimized.

private void assertConfigOptimized(LimeConfigOptimizer limeConfigOptimizer) throws InterruptedException, java.util.concurrent.ExecutionException {
    LimeConfig initialConfig = new LimeConfig().withSamples(10);
    PredictionProvider model = TestUtils.getSumSkipModel(1);
    Random random = new Random();
    random.setSeed(4);
    DataDistribution dataDistribution = DataUtils.generateRandomDataDistribution(5, 100, random);
    List<PredictionInput> samples = dataDistribution.sample(10);
    List<PredictionOutput> predictionOutputs = model.predictAsync(samples).get();
    List<Prediction> predictions = DataUtils.getPredictions(samples, predictionOutputs);
    LimeConfig optimizedConfig = limeConfigOptimizer.optimize(initialConfig, predictions, model);
    assertThat(optimizedConfig).isNotNull();
    Assertions.assertThat(optimizedConfig).isNotSameAs(initialConfig);
}

Example 65 with PredictionOutput

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

the class LimeImpactScoreCalculatorTest method testNonZeroScore.

@Test
void testNonZeroScore() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = TestUtils.getDummyTextClassifier();
    LimeImpactScoreCalculator scoreCalculator = new LimeImpactScoreCalculator();
    LimeConfig config = new LimeConfig();
    List<Feature> features = List.of(FeatureFactory.newFulltextFeature("text", "money so they say is the root of all evil today"));
    PredictionInput input = new PredictionInput(features);
    List<PredictionOutput> predictionOutputs = model.predictAsync(List.of(input)).get(Config.DEFAULT_ASYNC_TIMEOUT, Config.DEFAULT_ASYNC_TIMEUNIT);
    assertThat(predictionOutputs).isNotNull();
    assertThat(predictionOutputs.size()).isEqualTo(1);
    PredictionOutput output = predictionOutputs.get(0);
    Prediction prediction = new SimplePrediction(input, output);
    List<Prediction> predictions = List.of(prediction);
    List<LimeConfigEntity> entities = LimeConfigEntityFactory.createEncodingEntities(config);
    LimeConfigSolution solution = new LimeConfigSolution(config, predictions, entities, model);
    SimpleBigDecimalScore score = scoreCalculator.calculateScore(solution);
    assertThat(score).isNotNull();
    assertThat(score.getScore()).isNotNull().isNotEqualTo(BigDecimal.valueOf(0));
}