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

Example 16 with Prediction

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

the class DummyDmnModelsLimeExplainerTest method testAllTypesDMNExplanation.

@Test
void testAllTypesDMNExplanation() throws ExecutionException, InterruptedException, TimeoutException {
    DMNRuntime dmnRuntime = DMNKogito.createGenericDMNRuntime(new InputStreamReader(getClass().getResourceAsStream("/dmn/allTypes.dmn")));
    assertThat(dmnRuntime.getModels().size()).isEqualTo(1);
    final String namespace = "https://kiegroup.org/dmn/_24B9EC8C-2F02-40EB-B6BB-E8CDE82FBF08";
    final String name = "new-file";
    DecisionModel decisionModel = new DmnDecisionModel(dmnRuntime, namespace, name);
    PredictionProvider model = new DecisionModelWrapper(decisionModel);
    Map<String, Object> context = new HashMap<>();
    context.put("stringInput", "test");
    context.put("listOfStringInput", Collections.singletonList("test"));
    context.put("numberInput", 1);
    context.put("listOfNumbersInput", Collections.singletonList(1));
    context.put("booleanInput", true);
    context.put("listOfBooleansInput", Collections.singletonList(true));
    context.put("timeInput", "h09:00");
    context.put("dateInput", "2020-04-02");
    context.put("dateAndTimeInput", "2020-04-02T09:00:00");
    context.put("daysAndTimeDurationInput", "P1DT1H");
    context.put("yearsAndMonthDurationInput", "P1Y1M");
    Map<String, Object> complexInput = new HashMap<>();
    complexInput.put("aNestedListOfNumbers", Collections.singletonList(1));
    complexInput.put("aNestedString", "test");
    complexInput.put("aNestedComplexInput", Collections.singletonMap("doubleNestedNumber", 1));
    context.put("complexInput", complexInput);
    context.put("listOfComplexInput", Collections.singletonList(complexInput));
    List<Feature> features = new ArrayList<>();
    features.add(FeatureFactory.newCompositeFeature("context", context));
    PredictionInput predictionInput = new PredictionInput(features);
    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, 3);
    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()) {
        assertThat(saliency).isNotNull();
    }
    assertThatCode(() -> ValidationUtils.validateLocalSaliencyStability(model, prediction, limeExplainer, 1, 0.5, 0.2)).doesNotThrowAnyException();
    String decision = "myDecision";
    List<PredictionInput> inputs = new ArrayList<>();
    for (int n = 0; n < 10; n++) {
        inputs.add(new PredictionInput(DataUtils.perturbFeatures(features, perturbationContext)));
    }
    DataDistribution distribution = new PredictionInputsDataDistribution(inputs);
    int k = 2;
    int chunkSize = 5;
    double precision = ExplainabilityMetrics.getLocalSaliencyPrecision(decision, model, limeExplainer, distribution, k, chunkSize);
    assertThat(precision).isBetween(0d, 1d);
    double recall = ExplainabilityMetrics.getLocalSaliencyRecall(decision, model, limeExplainer, distribution, k, chunkSize);
    assertThat(recall).isBetween(0d, 1d);
    double f1 = ExplainabilityMetrics.getLocalSaliencyF1(decision, model, limeExplainer, distribution, k, chunkSize);
    assertThat(f1).isBetween(0d, 1d);
}

Example 17 with Prediction

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

the class DummyDmnModelsLimeExplainerTest method testFunctional1DMNExplanation.

@Test
void testFunctional1DMNExplanation() throws ExecutionException, InterruptedException, TimeoutException {
    DMNRuntime dmnRuntime = DMNKogito.createGenericDMNRuntime(new InputStreamReader(getClass().getResourceAsStream("/dmn/functionalTest1.dmn")));
    assertThat(dmnRuntime.getModels().size()).isEqualTo(1);
    final String namespace = "https://kiegroup.org/dmn/_049CD980-1310-4B02-9E90-EFC57059F44A";
    final String name = "functionalTest1";
    DecisionModel decisionModel = new DmnDecisionModel(dmnRuntime, namespace, name);
    PredictionProvider model = new DecisionModelWrapper(decisionModel);
    Map<String, Object> context = new HashMap<>();
    context.put("booleanInput", true);
    context.put("notUsedInput", 1);
    List<Feature> features = new ArrayList<>();
    features.add(FeatureFactory.newCompositeFeature("context", context));
    PredictionInput predictionInput = new PredictionInput(features);
    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()) {
        assertThat(saliency).isNotNull();
        List<FeatureImportance> topFeatures = saliency.getPositiveFeatures(2);
        assertThat(topFeatures.isEmpty()).isFalse();
        assertThat(topFeatures.get(0).getFeature().getName()).isEqualTo("booleanInput");
    }
    assertThatCode(() -> ValidationUtils.validateLocalSaliencyStability(model, prediction, limeExplainer, 1, 0.5, 0.5)).doesNotThrowAnyException();
    String decision = "decision";
    List<PredictionInput> inputs = new ArrayList<>();
    for (int n = 0; n < 10; n++) {
        inputs.add(new PredictionInput(DataUtils.perturbFeatures(features, perturbationContext)));
    }
    DataDistribution distribution = new PredictionInputsDataDistribution(inputs);
    int k = 2;
    int chunkSize = 5;
    double precision = ExplainabilityMetrics.getLocalSaliencyPrecision(decision, model, limeExplainer, distribution, k, chunkSize);
    assertThat(precision).isBetween(0d, 1d);
    double recall = ExplainabilityMetrics.getLocalSaliencyRecall(decision, model, limeExplainer, distribution, k, chunkSize);
    assertThat(recall).isBetween(0d, 1d);
    double f1 = ExplainabilityMetrics.getLocalSaliencyF1(decision, model, limeExplainer, distribution, k, chunkSize);
    assertThat(f1).isBetween(0d, 1d);
}

Example 18 with Prediction

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

the class DummyDmnModelsLimeExplainerTest method testFunctional2DMNExplanation.

@Test
void testFunctional2DMNExplanation() throws ExecutionException, InterruptedException, TimeoutException {
    DMNRuntime dmnRuntime = DMNKogito.createGenericDMNRuntime(new InputStreamReader(getClass().getResourceAsStream("/dmn/functionalTest2.dmn")));
    assertThat(dmnRuntime.getModels().size()).isEqualTo(1);
    final String namespace = "https://kiegroup.org/dmn/_049CD980-1310-4B02-9E90-EFC57059F44A";
    final String name = "new-file";
    DecisionModel decisionModel = new DmnDecisionModel(dmnRuntime, namespace, name);
    PredictionProvider model = new DecisionModelWrapper(decisionModel);
    Map<String, Object> context = new HashMap<>();
    context.put("numberInput", 1);
    context.put("notUsedInput", 1);
    List<Feature> features = new ArrayList<>();
    features.add(FeatureFactory.newCompositeFeature("context", context));
    PredictionInput predictionInput = new PredictionInput(features);
    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()) {
        assertThat(saliency).isNotNull();
        List<FeatureImportance> topFeatures = saliency.getPositiveFeatures(2);
        assertThat(topFeatures.isEmpty()).isFalse();
        assertThat(topFeatures.get(0).getFeature().getName()).isEqualTo("numberInput");
    }
    assertThatCode(() -> ValidationUtils.validateLocalSaliencyStability(model, prediction, limeExplainer, 1, 0.5, 0.5)).doesNotThrowAnyException();
    String decision = "decision";
    List<PredictionInput> inputs = new ArrayList<>();
    for (int n = 0; n < 10; n++) {
        inputs.add(new PredictionInput(DataUtils.perturbFeatures(features, perturbationContext)));
    }
    DataDistribution distribution = new PredictionInputsDataDistribution(inputs);
    int k = 2;
    int chunkSize = 5;
    double precision = ExplainabilityMetrics.getLocalSaliencyPrecision(decision, model, limeExplainer, distribution, k, chunkSize);
    assertThat(precision).isBetween(0d, 1d);
    double recall = ExplainabilityMetrics.getLocalSaliencyRecall(decision, model, limeExplainer, distribution, k, chunkSize);
    assertThat(recall).isBetween(0d, 1d);
    double f1 = ExplainabilityMetrics.getLocalSaliencyF1(decision, model, limeExplainer, distribution, k, chunkSize);
    assertThat(f1).isBetween(0d, 1d);
}

Example 19 with Prediction

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

the class FraudScoringDmnLimeExplainerTest method testExplanationImpactScoreWithOptimization.

@Test
void testExplanationImpactScoreWithOptimization() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = getModel();
    List<PredictionInput> samples = DmnTestUtils.randomFraudScoringInputs();
    List<PredictionOutput> predictionOutputs = model.predictAsync(samples.subList(0, 10)).get();
    List<Prediction> predictions = DataUtils.getPredictions(samples, predictionOutputs);
    long seed = 0;
    LimeConfigOptimizer limeConfigOptimizer = new LimeConfigOptimizer().withDeterministicExecution(true).forImpactScore().withSampling(false).withStepCountLimit(20);
    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);
}

Example 20 with Prediction

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

the class FraudScoringDmnLimeExplainerTest method testExplanationStabilityWithOptimization.

@Test
void testExplanationStabilityWithOptimization() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = getModel();
    List<PredictionInput> samples = DmnTestUtils.randomFraudScoringInputs();
    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).withStepCountLimit(20);
    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, 0.5, 0.5));
}