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

Example 61 with SimplePrediction

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

the class LimeExplainerServiceHandlerTest method testGetPredictionWithEmptyDefinition.

@Test
public void testGetPredictionWithEmptyDefinition() {
    LIMEExplainabilityRequest request = new LIMEExplainabilityRequest(EXECUTION_ID, SERVICE_URL, MODEL_IDENTIFIER, Collections.emptyList(), Collections.emptyList());
    Prediction prediction = handler.getPrediction(request);
    assertTrue(prediction instanceof SimplePrediction);
    SimplePrediction simplePrediction = (SimplePrediction) prediction;
    assertTrue(simplePrediction.getInput().getFeatures().isEmpty());
    assertTrue(simplePrediction.getOutput().getOutputs().isEmpty());
}

Example 62 with SimplePrediction

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

the class TrafficViolationDmnLimeExplainerTest method testTrafficViolationDMNExplanation.

@Test
void testTrafficViolationDMNExplanation() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = getModel();
    PredictionInput predictionInput = getTestInput();
    List<PredictionOutput> predictionOutputs = model.predictAsync(List.of(predictionInput)).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    Prediction prediction = new SimplePrediction(predictionInput, predictionOutputs.get(0));
    Random random = new Random();
    PerturbationContext perturbationContext = new PerturbationContext(0L, random, 1);
    LimeConfig limeConfig = new LimeConfig().withSamples(10).withPerturbationContext(perturbationContext);
    LimeExplainer limeExplainer = new LimeExplainer(limeConfig);
    Map<String, Saliency> saliencyMap = limeExplainer.explainAsync(prediction, model).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    for (Saliency saliency : saliencyMap.values()) {
        assertNotNull(saliency);
        List<String> strings = saliency.getTopFeatures(3).stream().map(f -> f.getFeature().getName()).collect(Collectors.toList());
        assertTrue(strings.contains("Actual Speed") || strings.contains("Speed Limit"));
    }
    assertDoesNotThrow(() -> ValidationUtils.validateLocalSaliencyStability(model, prediction, limeExplainer, 1, 0.3, 0.3));
    String decision = "Fine";
    List<PredictionInput> inputs = new ArrayList<>();
    for (int n = 0; n < 10; n++) {
        inputs.add(new PredictionInput(DataUtils.perturbFeatures(predictionInput.getFeatures(), perturbationContext)));
    }
    DataDistribution distribution = new PredictionInputsDataDistribution(inputs);
    int k = 2;
    int chunkSize = 5;
    double f1 = ExplainabilityMetrics.getLocalSaliencyF1(decision, model, limeExplainer, distribution, k, chunkSize);
    AssertionsForClassTypes.assertThat(f1).isBetween(0.5d, 1d);
}

Example 63 with SimplePrediction

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

the class TrafficViolationDmnPDPExplainerTest method testTrafficViolationDMNExplanation.

@Test
void testTrafficViolationDMNExplanation() throws ExecutionException, InterruptedException, TimeoutException {
    DMNRuntime dmnRuntime = DMNKogito.createGenericDMNRuntime(new InputStreamReader(getClass().getResourceAsStream("/dmn/TrafficViolation.dmn")));
    assertEquals(1, dmnRuntime.getModels().size());
    final String TRAFFIC_VIOLATION_NS = "https://github.com/kiegroup/drools/kie-dmn/_A4BCA8B8-CF08-433F-93B2-A2598F19ECFF";
    final String TRAFFIC_VIOLATION_NAME = "Traffic Violation";
    DecisionModel decisionModel = new DmnDecisionModel(dmnRuntime, TRAFFIC_VIOLATION_NS, TRAFFIC_VIOLATION_NAME);
    PredictionProvider model = new DecisionModelWrapper(decisionModel);
    List<PredictionInput> inputs = DmnTestUtils.randomTrafficViolationInputs();
    List<PredictionOutput> predictionOutputs = model.predictAsync(inputs).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    List<Prediction> predictions = new ArrayList<>();
    for (int i = 0; i < predictionOutputs.size(); i++) {
        predictions.add(new SimplePrediction(inputs.get(i), predictionOutputs.get(i)));
    }
    PartialDependencePlotExplainer partialDependencePlotExplainer = new PartialDependencePlotExplainer();
    List<PartialDependenceGraph> pdps = partialDependencePlotExplainer.explainFromPredictions(model, predictions);
    AssertionsForClassTypes.assertThat(pdps).isNotNull();
    Assertions.assertThat(pdps).hasSize(8);
}

Example 64 with SimplePrediction

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

the class PmmlCompoundScorecardLimeExplainerTest method testExplanationStabilityWithOptimization.

@Test
void testExplanationStabilityWithOptimization() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = getModel();
    List<PredictionInput> samples = getSamples();
    List<PredictionOutput> predictionOutputs = model.predictAsync(samples.subList(0, 5)).get();
    List<Prediction> predictions = DataUtils.getPredictions(samples, predictionOutputs);
    long seed = 0;
    LimeConfigOptimizer limeConfigOptimizer = new LimeConfigOptimizer().withDeterministicExecution(true);
    Random random = new Random();
    PerturbationContext perturbationContext = new PerturbationContext(seed, random, 1);
    LimeConfig initialConfig = new LimeConfig().withSamples(10).withPerturbationContext(perturbationContext);
    LimeConfig optimizedConfig = limeConfigOptimizer.optimize(initialConfig, predictions, model);
    assertThat(optimizedConfig).isNotSameAs(initialConfig);
    LimeExplainer limeExplainer = new LimeExplainer(optimizedConfig);
    PredictionInput testPredictionInput = getTestInput();
    List<PredictionOutput> testPredictionOutputs = model.predictAsync(List.of(testPredictionInput)).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    Prediction instance = new SimplePrediction(testPredictionInput, testPredictionOutputs.get(0));
    assertDoesNotThrow(() -> ValidationUtils.validateLocalSaliencyStability(model, instance, limeExplainer, 1, 0.6, 0.6));
}

Example 65 with SimplePrediction

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

the class PmmlRegressionCategoricalLimeExplainerTest method testExplanationWeightedStabilityWithOptimization.

@Disabled("See KOGITO-6154")
@Test
void testExplanationWeightedStabilityWithOptimization() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = getModel();
    List<PredictionInput> samples = getSamples();
    List<PredictionOutput> predictionOutputs = model.predictAsync(samples.subList(0, 5)).get();
    List<Prediction> predictions = DataUtils.getPredictions(samples, predictionOutputs);
    long seed = 0;
    LimeConfigOptimizer limeConfigOptimizer = new LimeConfigOptimizer().withDeterministicExecution(true).withWeightedStability(0.4, 0.6);
    Random random = new Random();
    random.setSeed(seed);
    PerturbationContext perturbationContext = new PerturbationContext(seed, random, 1);
    LimeConfig initialConfig = new LimeConfig().withSamples(10).withPerturbationContext(perturbationContext);
    LimeConfig optimizedConfig = limeConfigOptimizer.optimize(initialConfig, predictions, model);
    assertThat(optimizedConfig).isNotSameAs(initialConfig);
    LimeExplainer limeExplainer = new LimeExplainer(optimizedConfig);
    PredictionInput testPredictionInput = getTestInput();
    List<PredictionOutput> testPredictionOutputs = model.predictAsync(List.of(testPredictionInput)).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    Prediction instance = new SimplePrediction(testPredictionInput, testPredictionOutputs.get(0));
    assertDoesNotThrow(() -> ValidationUtils.validateLocalSaliencyStability(model, instance, limeExplainer, 1, 0.5, 0.7));
}