Examples with LimeExplainer org.kie.kogito.explainability.local.lime.LimeExplainer used on opensource projects

Example 21 with LimeExplainer

use of org.kie.kogito.explainability.local.lime.LimeExplainer in project kogito-apps by kiegroup.

the class PmmlScorecardCategoricalLimeExplainerTest method testExplanationWeightedStabilityWithOptimization.

@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();
    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));
}

Example 22 with LimeExplainer

use of org.kie.kogito.explainability.local.lime.LimeExplainer in project kogito-apps by kiegroup.

the class PmmlScorecardCategoricalLimeExplainerTest method testPMMLScorecardCategorical.

@Test
void testPMMLScorecardCategorical() throws Exception {
    PredictionInput input = getTestInput();
    Random random = new Random();
    LimeConfig limeConfig = new LimeConfig().withSamples(10).withPerturbationContext(new PerturbationContext(0L, random, 1));
    LimeExplainer limeExplainer = new LimeExplainer(limeConfig);
    PredictionProvider model = getModel();
    List<PredictionOutput> predictionOutputs = model.predictAsync(List.of(input)).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    assertThat(predictionOutputs).isNotNull().isNotEmpty();
    PredictionOutput output = predictionOutputs.get(0);
    assertThat(output).isNotNull();
    Prediction prediction = new SimplePrediction(input, output);
    Map<String, Saliency> saliencyMap = limeExplainer.explainAsync(prediction, model).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    for (Saliency saliency : saliencyMap.values()) {
        assertThat(saliency).isNotNull();
        double v = ExplainabilityMetrics.impactScore(model, prediction, saliency.getTopFeatures(2));
        assertThat(v).isGreaterThan(0d);
    }
    assertDoesNotThrow(() -> ValidationUtils.validateLocalSaliencyStability(model, prediction, limeExplainer, 1, 0.4, 0.4));
    List<PredictionInput> inputs = getSamples();
    DataDistribution distribution = new PredictionInputsDataDistribution(inputs);
    String decision = "score";
    int k = 1;
    int chunkSize = 2;
    double f1 = ExplainabilityMetrics.getLocalSaliencyF1(decision, model, limeExplainer, distribution, k, chunkSize);
    AssertionsForClassTypes.assertThat(f1).isBetween(0d, 1d);
}

Example 23 with LimeExplainer

use of org.kie.kogito.explainability.local.lime.LimeExplainer in project kogito-apps by kiegroup.

the class CountingOptimizationStrategyTest method testMaybeOptimize.

@Test
void testMaybeOptimize() {
    LimeOptimizationService optimizationService = mock(LimeOptimizationService.class);
    CountingOptimizationStrategy strategy = new CountingOptimizationStrategy(10, optimizationService);
    List<Prediction> recordedPredictions = Collections.emptyList();
    PredictionProvider model = mock(PredictionProvider.class);
    LimeExplainer explaier = new LimeExplainer();
    LimeConfig config = new LimeConfig();
    assertThatCode(() -> strategy.maybeOptimize(recordedPredictions, model, explaier, config)).doesNotThrowAnyException();
}

Example 24 with LimeExplainer

use of org.kie.kogito.explainability.local.lime.LimeExplainer in project kogito-apps by kiegroup.

the class ExplainabilityMetricsTest method testFidelityWithEvenSumModel.

@Test
void testFidelityWithEvenSumModel() throws ExecutionException, InterruptedException, TimeoutException {
    List<Pair<Saliency, Prediction>> pairs = new LinkedList<>();
    LimeConfig limeConfig = new LimeConfig().withSamples(10);
    LimeExplainer limeExplainer = new LimeExplainer(limeConfig);
    PredictionProvider model = TestUtils.getEvenSumModel(1);
    List<Feature> features = new LinkedList<>();
    features.add(FeatureFactory.newNumericalFeature("f-1", 1));
    features.add(FeatureFactory.newNumericalFeature("f-2", 2));
    features.add(FeatureFactory.newNumericalFeature("f-3", 3));
    PredictionInput input = new PredictionInput(features);
    Prediction prediction = new SimplePrediction(input, model.predictAsync(List.of(input)).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit()).get(0));
    Map<String, Saliency> saliencyMap = limeExplainer.explainAsync(prediction, model).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    for (Saliency saliency : saliencyMap.values()) {
        pairs.add(Pair.of(saliency, prediction));
    }
    Assertions.assertDoesNotThrow(() -> {
        ExplainabilityMetrics.classificationFidelity(pairs);
    });
}

Example 25 with LimeExplainer

use of org.kie.kogito.explainability.local.lime.LimeExplainer in project kogito-apps by kiegroup.

the class CountingOptimizationStrategyTest method testNullConfig.

@Test
void testNullConfig() {
    LimeOptimizationService optimizationService = mock(LimeOptimizationService.class);
    CountingOptimizationStrategy strategy = new CountingOptimizationStrategy(10, optimizationService);
    assertThat(strategy.bestConfigFor(new LimeExplainer())).isNull();
}