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

Example 36 with SimplePrediction

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

the class ShapKernelExplainerTest method testManyFeatureRegularization2.

@Test
void testManyFeatureRegularization2() throws ExecutionException, InterruptedException {
    RealVector modelWeights = MatrixUtils.createRealMatrix(generateN(1, 25, "5021")).getRowVector(0);
    PredictionProvider model = TestUtils.getLinearModel(modelWeights.toArray());
    RealMatrix data = MatrixUtils.createRealMatrix(generateN(101, 25, "8629"));
    List<PredictionInput> toExplain = createPIFromMatrix(data.getRowMatrix(100).getData());
    List<PredictionOutput> predictionOutputs = model.predictAsync(toExplain).get();
    RealVector predictionOutputVector = MatrixUtilsExtensions.vectorFromPredictionOutput(predictionOutputs.get(0));
    Prediction p = new SimplePrediction(toExplain.get(0), predictionOutputs.get(0));
    List<PredictionInput> bg = createPIFromMatrix(new double[100][25]);
    ShapConfig sk = testConfig.copy().withBackground(bg).withRegularizer(ShapConfig.RegularizerType.AIC).withNSamples(5000).build();
    ShapKernelExplainer ske = new ShapKernelExplainer(sk);
    ShapResults shapResults = ske.explainAsync(p, model).get();
    Saliency[] saliencies = shapResults.getSaliencies();
    RealMatrix[] explanationsAndConfs = saliencyToMatrix(saliencies);
    RealMatrix explanations = explanationsAndConfs[0];
    double actualOut = predictionOutputVector.getEntry(0);
    double predOut = MatrixUtilsExtensions.sum(explanations.getRowVector(0)) + shapResults.getFnull().getEntry(0);
    assertTrue(Math.abs(predOut - actualOut) < 1e-6);
    double coefMSE = (data.getRowVector(100).ebeMultiply(modelWeights)).getDistance(explanations.getRowVector(0));
    assertTrue(coefMSE < .01);
}

Example 37 with SimplePrediction

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

the class ExplainabilityMetricsTest method testFidelityWithTextClassifier.

@Test
void testFidelityWithTextClassifier() 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.getDummyTextClassifier();
    List<Feature> features = new LinkedList<>();
    features.add(FeatureFactory.newFulltextFeature("f-0", "brown fox", s -> Arrays.asList(s.split(" "))));
    features.add(FeatureFactory.newTextFeature("f-1", "money"));
    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 38 with SimplePrediction

use of org.kie.kogito.explainability.model.SimplePrediction 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 39 with SimplePrediction

use of org.kie.kogito.explainability.model.SimplePrediction 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 40 with SimplePrediction

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

the class LimeExplainerTest method testWithDataDistribution.

@Test
void testWithDataDistribution() throws InterruptedException, ExecutionException, TimeoutException {
    Random random = new Random();
    PerturbationContext perturbationContext = new PerturbationContext(4L, random, 1);
    List<FeatureDistribution> featureDistributions = new ArrayList<>();
    int nf = 4;
    List<Feature> features = new ArrayList<>();
    for (int i = 0; i < nf; i++) {
        Feature numericalFeature = FeatureFactory.newNumericalFeature("f-" + i, Double.NaN);
        features.add(numericalFeature);
        List<Value> values = new ArrayList<>();
        for (int r = 0; r < 4; r++) {
            values.add(Type.NUMBER.randomValue(perturbationContext));
        }
        featureDistributions.add(new GenericFeatureDistribution(numericalFeature, values));
    }
    DataDistribution dataDistribution = new IndependentFeaturesDataDistribution(featureDistributions);
    LimeConfig limeConfig = new LimeConfig().withDataDistribution(dataDistribution).withPerturbationContext(perturbationContext).withSamples(10);
    LimeExplainer limeExplainer = new LimeExplainer(limeConfig);
    PredictionInput input = new PredictionInput(features);
    PredictionProvider model = TestUtils.getSumThresholdModel(random.nextDouble(), random.nextDouble());
    PredictionOutput output = model.predictAsync(List.of(input)).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit()).get(0);
    Prediction prediction = new SimplePrediction(input, output);
    Map<String, Saliency> saliencyMap = limeExplainer.explainAsync(prediction, model).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    assertThat(saliencyMap).isNotNull();
    String decisionName = "inside";
    Saliency saliency = saliencyMap.get(decisionName);
    assertThat(saliency).isNotNull();
}