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

Example 91 with PredictionInput

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

the class DataUtilsTest method testTexify.

@Test
void testTexify() {
    List<Feature> features = new ArrayList<>();
    features.add(TestUtils.getMockedTextFeature("we go here and there"));
    features.add(TestUtils.getMockedTextFeature("as you go there and here"));
    PredictionInput input = new PredictionInput(features);
    String textifiedInput = DataUtils.textify(input);
    assertThat(textifiedInput).isNotNull().isEqualTo("we go here and there as you go there and here");
}

Example 92 with PredictionInput

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

the class DataUtilsTest method testPerturbDropString.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2, 3 })
void testPerturbDropString(int param) {
    List<Feature> features = new LinkedList<>();
    features.add(FeatureFactory.newTextFeature("f0", "foo"));
    features.add(FeatureFactory.newTextFeature("f1", "foo bar"));
    features.add(FeatureFactory.newTextFeature("f2", " "));
    features.add(FeatureFactory.newTextFeature("f3", "foo bar "));
    PredictionInput input = new PredictionInput(features);
    assertPerturbDropString(input, param);
}

Example 93 with PredictionInput

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

the class DataUtilsTest method testPerturbDropNumeric.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2, 3 })
void testPerturbDropNumeric(int param) {
    List<Feature> features = new LinkedList<>();
    features.add(FeatureFactory.newNumericalFeature("f0", 1));
    features.add(FeatureFactory.newNumericalFeature("f1", 3.14));
    features.add(FeatureFactory.newNumericalFeature("f2", 0.55));
    PredictionInput input = new PredictionInput(features);
    assertPerturbDropNumeric(input, param);
}

Example 94 with PredictionInput

use of org.kie.kogito.explainability.model.PredictionInput 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 95 with PredictionInput

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

the class PmmlScorecardCategoricalLimeExplainerTest method testExplanationImpactScoreWithOptimization.

@Test
void testExplanationImpactScoreWithOptimization() throws ExecutionException, InterruptedException {
    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).forImpactScore();
    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);
}