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

Example 6 with FeatureImportance

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

the class LimeExplainerServiceHandlerTest method testCreateSucceededResult.

@Test
public void testCreateSucceededResult() {
    LIMEExplainabilityRequest request = new LIMEExplainabilityRequest(EXECUTION_ID, SERVICE_URL, MODEL_IDENTIFIER, Collections.emptyList(), Collections.emptyList());
    Map<String, Saliency> saliencies = Map.of("s1", new Saliency(new Output("salary", Type.NUMBER), List.of(new FeatureImportance(new Feature("age", Type.NUMBER, new Value(25.0)), 5.0), new FeatureImportance(new Feature("dependents", Type.NUMBER, new Value(2)), -11.0))));
    BaseExplainabilityResult base = handler.createSucceededResult(request, saliencies);
    assertTrue(base instanceof LIMEExplainabilityResult);
    LIMEExplainabilityResult result = (LIMEExplainabilityResult) base;
    assertEquals(ExplainabilityStatus.SUCCEEDED, result.getStatus());
    assertEquals(EXECUTION_ID, result.getExecutionId());
    assertEquals(1, result.getSaliencies().size());
    SaliencyModel saliencyModel = result.getSaliencies().iterator().next();
    assertEquals(2, saliencyModel.getFeatureImportance().size());
    assertEquals("age", saliencyModel.getFeatureImportance().get(0).getFeatureName());
    assertEquals(5.0, saliencyModel.getFeatureImportance().get(0).getFeatureScore());
    assertEquals("dependents", saliencyModel.getFeatureImportance().get(1).getFeatureName());
    assertEquals(-11.0, saliencyModel.getFeatureImportance().get(1).getFeatureScore());
}

Example 7 with FeatureImportance

use of org.kie.kogito.explainability.model.FeatureImportance 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 8 with FeatureImportance

use of org.kie.kogito.explainability.model.FeatureImportance 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 9 with FeatureImportance

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

the class FraudScoringDmnLimeExplainerTest method testFraudScoringDMNExplanation.

@Test
void testFraudScoringDMNExplanation() 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<FeatureImportance> topFeatures = saliency.getTopFeatures(4);
        double topScore = Math.abs(topFeatures.stream().map(FeatureImportance::getScore).findFirst().orElse(0d));
        if (!topFeatures.isEmpty() && topScore > 0) {
            double v = ExplainabilityMetrics.impactScore(model, prediction, topFeatures);
            // checks the drop of important features triggers a flipped prediction (or a significant drop in the output score).
            assertThat(v).isPositive();
        }
    }
    assertDoesNotThrow(() -> ValidationUtils.validateLocalSaliencyStability(model, prediction, limeExplainer, 1, 0.4, // set to 0.4 since "Last Transaction" is inherently unstable output
    0.4));
    String decision = "Risk Score";
    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 = 2;
    double f1 = ExplainabilityMetrics.getLocalSaliencyF1(decision, model, limeExplainer, distribution, k, chunkSize);
    AssertionsForClassTypes.assertThat(f1).isBetween(0.5d, 1d);
}

Example 10 with FeatureImportance

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

the class DummyModelsLimeExplainerTest method testUnusedFeatureClassification.

@ParameterizedTest
@ValueSource(longs = { 0 })
void testUnusedFeatureClassification(long seed) throws Exception {
    Random random = new Random();
    int idx = 2;
    List<Feature> features = new LinkedList<>();
    features.add(FeatureFactory.newNumericalFeature("f1", 6));
    features.add(FeatureFactory.newNumericalFeature("f2", 3));
    features.add(FeatureFactory.newNumericalFeature("f3", 5));
    PredictionProvider model = TestUtils.getEvenSumModel(idx);
    PredictionInput input = new PredictionInput(features);
    List<PredictionOutput> outputs = model.predictAsync(List.of(input)).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    Prediction prediction = new SimplePrediction(input, outputs.get(0));
    LimeConfig limeConfig = new LimeConfig().withSamples(100).withPerturbationContext(new PerturbationContext(seed, random, 1));
    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<FeatureImportance> topFeatures = saliency.getTopFeatures(3);
        assertEquals(3, topFeatures.size());
        assertEquals(1d, ExplainabilityMetrics.impactScore(model, prediction, topFeatures));
    }
    int topK = 1;
    double minimumPositiveStabilityRate = 0.5;
    double minimumNegativeStabilityRate = 0.5;
    TestUtils.assertLimeStability(model, prediction, limeExplainer, topK, minimumPositiveStabilityRate, minimumNegativeStabilityRate);
    List<PredictionInput> inputs = new ArrayList<>();
    for (int i = 0; i < 100; i++) {
        List<Feature> fs = new LinkedList<>();
        fs.add(TestUtils.getMockedNumericFeature());
        fs.add(TestUtils.getMockedNumericFeature());
        fs.add(TestUtils.getMockedNumericFeature());
        inputs.add(new PredictionInput(fs));
    }
    DataDistribution distribution = new PredictionInputsDataDistribution(inputs);
    int k = 2;
    int chunkSize = 10;
    String decision = "sum-even-but" + idx;
    double precision = ExplainabilityMetrics.getLocalSaliencyPrecision(decision, model, limeExplainer, distribution, k, chunkSize);
    assertThat(precision).isEqualTo(1);
    double recall = ExplainabilityMetrics.getLocalSaliencyRecall(decision, model, limeExplainer, distribution, k, chunkSize);
    assertThat(recall).isEqualTo(1);
    double f1 = ExplainabilityMetrics.getLocalSaliencyF1(decision, model, limeExplainer, distribution, k, chunkSize);
    assertThat(f1).isEqualTo(1);
}