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

Example 46 with PerturbationContext

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

the class HighScoreNumericFeatureZonesProviderTest method testNonEmptyData.

@Test
void testNonEmptyData() {
    Random random = new Random();
    random.setSeed(0);
    PerturbationContext perturbationContext = new PerturbationContext(random, 1);
    List<Feature> features = new ArrayList<>();
    PredictionProvider predictionProvider = TestUtils.getSumThresholdModel(0.1, 0.1);
    List<FeatureDistribution> featureDistributions = new ArrayList<>();
    int nf = 4;
    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);
    Map<String, HighScoreNumericFeatureZones> highScoreFeatureZones = HighScoreNumericFeatureZonesProvider.getHighScoreFeatureZones(dataDistribution, predictionProvider, features, 10);
    assertThat(highScoreFeatureZones).isNotNull();
    assertThat(highScoreFeatureZones.size()).isEqualTo(4);
}

Example 47 with PerturbationContext

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

the class DataUtilsTest method testBootstrap.

@Test
void testBootstrap() {
    List<Value> values = new ArrayList<>();
    PerturbationContext perturbationContext = new PerturbationContext(random, 1);
    for (int i = 0; i < 4; i++) {
        values.add(Type.NUMBER.randomValue(perturbationContext));
    }
    Feature mockedNumericFeature = TestUtils.getMockedNumericFeature();
    DataDistribution dataDistribution = new IndependentFeaturesDataDistribution(List.of(new GenericFeatureDistribution(mockedNumericFeature, values)));
    Map<String, FeatureDistribution> featureDistributionMap = DataUtils.boostrapFeatureDistributions(dataDistribution, perturbationContext, 10, 1, 500, new HashMap<>());
    assertThat(featureDistributionMap).isNotNull();
    assertThat(featureDistributionMap).isNotEmpty();
    FeatureDistribution actual = featureDistributionMap.get(mockedNumericFeature.getName());
    assertThat(actual).isNotNull();
    List<Value> allSamples = actual.getAllSamples();
    assertThat(allSamples).isNotNull();
    assertThat(allSamples).hasSize(10);
}

Example 48 with PerturbationContext

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

the class DataUtilsTest method assertPerturbDropString.

private void assertPerturbDropString(PredictionInput input, int noOfPerturbations) {
    List<Feature> newFeatures = DataUtils.perturbFeatures(input.getFeatures(), new PerturbationContext(random, noOfPerturbations));
    int changedFeatures = 0;
    for (int i = 0; i < input.getFeatures().size(); i++) {
        String v = input.getFeatures().get(i).getValue().asString();
        String pv = newFeatures.get(i).getValue().asString();
        if (!v.equals(pv)) {
            changedFeatures++;
        }
    }
    assertThat(changedFeatures).isBetween((int) Math.min(noOfPerturbations, input.getFeatures().size() * 0.5), (int) Math.max(noOfPerturbations, input.getFeatures().size() * 0.5));
}

Example 49 with PerturbationContext

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

the class LimeConfigOptimizerTest method testSameConfig.

@Test
void testSameConfig() throws ExecutionException, InterruptedException {
    long seed = 0;
    List<LimeConfig> optimizedConfigs = new ArrayList<>();
    PredictionProvider model = TestUtils.getSumSkipModel(1);
    DataDistribution dataDistribution = DataUtils.generateRandomDataDistribution(5, 100, new Random());
    List<PredictionInput> samples = dataDistribution.sample(3);
    List<PredictionOutput> predictionOutputs = model.predictAsync(samples).get();
    List<Prediction> predictions = DataUtils.getPredictions(samples, predictionOutputs);
    for (int i = 0; i < 2; i++) {
        Random random = new Random();
        LimeConfig initialConfig = new LimeConfig().withSamples(10).withPerturbationContext(new PerturbationContext(seed, random, 1));
        LimeConfigOptimizer limeConfigOptimizer = new LimeConfigOptimizer().withDeterministicExecution(true).withStepCountLimit(10).withTimeLimit(10);
        LimeConfig optimizedConfig = limeConfigOptimizer.optimize(initialConfig, predictions, model);
        optimizedConfigs.add(optimizedConfig);
    }
    LimeConfig first = optimizedConfigs.get(0);
    LimeConfig second = optimizedConfigs.get(1);
    assertThat(first.getNoOfRetries()).isEqualTo(second.getNoOfRetries());
    assertThat(first.getNoOfSamples()).isEqualTo(second.getNoOfSamples());
    assertThat(first.getProximityFilteredDatasetMinimum()).isEqualTo(second.getProximityFilteredDatasetMinimum());
    assertThat(first.getProximityKernelWidth()).isEqualTo(second.getProximityKernelWidth());
    assertThat(first.getProximityThreshold()).isEqualTo(second.getProximityThreshold());
    assertThat(first.isProximityFilter()).isEqualTo(second.isProximityFilter());
    assertThat(first.isAdaptDatasetVariance()).isEqualTo(second.isAdaptDatasetVariance());
    assertThat(first.isPenalizeBalanceSparse()).isEqualTo(second.isPenalizeBalanceSparse());
    assertThat(first.getEncodingParams().getNumericTypeClusterGaussianFilterWidth()).isEqualTo(second.getEncodingParams().getNumericTypeClusterGaussianFilterWidth());
    assertThat(first.getEncodingParams().getNumericTypeClusterThreshold()).isEqualTo(second.getEncodingParams().getNumericTypeClusterThreshold());
    assertThat(first.getSeparableDatasetRatio()).isEqualTo(second.getSeparableDatasetRatio());
    assertThat(first.getPerturbationContext().getNoOfPerturbations()).isEqualTo(second.getPerturbationContext().getNoOfPerturbations());
}

Example 50 with PerturbationContext

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

the class LimeExplainerTest method testDeterministic.

@ParameterizedTest
@ValueSource(longs = { 0, 1, 2, 3, 4 })
void testDeterministic(long seed) throws ExecutionException, InterruptedException, TimeoutException {
    List<Saliency> saliencies = new ArrayList<>();
    for (int j = 0; j < 2; j++) {
        Random random = new Random();
        LimeConfig limeConfig = new LimeConfig().withPerturbationContext(new PerturbationContext(seed, random, DEFAULT_NO_OF_PERTURBATIONS)).withSamples(10);
        LimeExplainer limeExplainer = new LimeExplainer(limeConfig);
        List<Feature> features = new ArrayList<>();
        for (int i = 0; i < 4; i++) {
            features.add(TestUtils.getMockedNumericFeature(i));
        }
        PredictionInput input = new PredictionInput(features);
        PredictionProvider model = TestUtils.getSumSkipModel(0);
        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());
        saliencies.add(saliencyMap.get("sum-but0"));
    }
    assertThat(saliencies.get(0).getPerFeatureImportance().stream().map(FeatureImportance::getScore).collect(Collectors.toList())).isEqualTo(saliencies.get(1).getPerFeatureImportance().stream().map(FeatureImportance::getScore).collect(Collectors.toList()));
}