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

Example 51 with Output

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

the class OpenNLPPDPExplainerTest method testOpenNLPLangDetect.

@Test
void testOpenNLPLangDetect() throws Exception {
    PartialDependencePlotExplainer partialDependencePlotExplainer = new PartialDependencePlotExplainer();
    InputStream is = getClass().getResourceAsStream("/opennlp/langdetect-183.bin");
    LanguageDetectorModel languageDetectorModel = new LanguageDetectorModel(is);
    LanguageDetector languageDetector = new LanguageDetectorME(languageDetectorModel);
    PredictionProvider model = inputs -> CompletableFuture.supplyAsync(() -> {
        List<PredictionOutput> results = new ArrayList<>();
        for (PredictionInput predictionInput : inputs) {
            StringBuilder builder = new StringBuilder();
            for (Feature f : predictionInput.getFeatures()) {
                if (builder.length() > 0) {
                    builder.append(' ');
                }
                builder.append(f.getValue().asString());
            }
            Language language = languageDetector.predictLanguage(builder.toString());
            PredictionOutput predictionOutput = new PredictionOutput(List.of(new Output("lang", Type.TEXT, new Value(language.getLang()), language.getConfidence())));
            results.add(predictionOutput);
        }
        return results;
    });
    List<String> texts = List.of("we want your money", "please reply quickly", "you are the lucky winner", "italiani, spaghetti pizza mandolino", "guten tag", "allez les bleus", "daje roma");
    List<Prediction> predictions = new ArrayList<>();
    for (String text : texts) {
        List<Feature> features = new ArrayList<>();
        features.add(FeatureFactory.newFulltextFeature("text", text));
        PredictionInput predictionInput = new PredictionInput(features);
        PredictionOutput predictionOutput = model.predictAsync(List.of(predictionInput)).get().get(0);
        predictions.add(new SimplePrediction(predictionInput, predictionOutput));
    }
    List<PartialDependenceGraph> pdps = partialDependencePlotExplainer.explainFromPredictions(model, predictions);
    assertThat(pdps).isNotEmpty();
}

Example 52 with Output

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

the class PmmlCompoundScorecardLimeExplainerTest method getModel.

private PredictionProvider getModel() {
    return inputs -> CompletableFuture.supplyAsync(() -> {
        List<PredictionOutput> outputs = new ArrayList<>(inputs.size());
        for (PredictionInput predictionInput : inputs) {
            List<Feature> inputFeatures = predictionInput.getFeatures();
            CompoundNestedPredicateScorecardExecutor pmmlModel = new CompoundNestedPredicateScorecardExecutor(inputFeatures.get(0).getValue().asNumber(), inputFeatures.get(1).getValue().asString());
            PMML4Result result = pmmlModel.execute(compoundScoreCardRuntime);
            Map<String, Object> resultVariables = result.getResultVariables();
            String score = "" + resultVariables.get(CompoundNestedPredicateScorecardExecutor.TARGET_FIELD);
            String reason1 = "" + resultVariables.get(CompoundNestedPredicateScorecardExecutor.REASON_CODE1_FIELD);
            PredictionOutput predictionOutput = new PredictionOutput(List.of(new Output("score", Type.TEXT, new Value(score), 1d), new Output("reason1", Type.TEXT, new Value(reason1), 1d)));
            outputs.add(predictionOutput);
        }
        return outputs;
    });
}

Example 53 with Output

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

the class CounterfactualScoreCalculatorTest method TextDistanceSameValue.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2, 3, 4 })
void TextDistanceSameValue(int seed) {
    final String value = UUID.randomUUID().toString();
    Feature x = FeatureFactory.newTextFeature("x", value);
    Feature y = FeatureFactory.newTextFeature("y", value);
    Output ox = outputFromFeature(x);
    Output oy = outputFromFeature(y);
    final double distance = CounterFactualScoreCalculator.outputDistance(ox, oy);
    assertEquals(Type.TEXT, ox.getType());
    assertEquals(0.0, distance);
}

Example 54 with Output

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

the class CounterfactualScoreCalculatorTest method testGoalSizeMatch.

/**
 * If the goal and the model's output is the same, the distances should all be zero.
 */
@Test
void testGoalSizeMatch() throws ExecutionException, InterruptedException {
    final CounterFactualScoreCalculator scoreCalculator = new CounterFactualScoreCalculator();
    PredictionProvider model = TestUtils.getFeatureSkipModel(0);
    List<Feature> features = new ArrayList<>();
    List<FeatureDomain> featureDomains = new ArrayList<>();
    List<Boolean> constraints = new ArrayList<>();
    // f-1
    features.add(FeatureFactory.newNumericalFeature("f-1", 1.0));
    featureDomains.add(NumericalFeatureDomain.create(0.0, 10.0));
    constraints.add(false);
    // f-2
    features.add(FeatureFactory.newNumericalFeature("f-2", 2.0));
    featureDomains.add(NumericalFeatureDomain.create(0.0, 10.0));
    constraints.add(false);
    // f-3
    features.add(FeatureFactory.newBooleanFeature("f-3", true));
    featureDomains.add(EmptyFeatureDomain.create());
    constraints.add(false);
    PredictionInput input = new PredictionInput(features);
    PredictionFeatureDomain domains = new PredictionFeatureDomain(featureDomains);
    List<CounterfactualEntity> entities = CounterfactualEntityFactory.createEntities(input);
    List<Output> goal = new ArrayList<>();
    goal.add(new Output("f-2", Type.NUMBER, new Value(2.0), 0.0));
    goal.add(new Output("f-3", Type.BOOLEAN, new Value(true), 0.0));
    final CounterfactualSolution solution = new CounterfactualSolution(entities, features, model, goal, UUID.randomUUID(), UUID.randomUUID(), 0.0);
    BendableBigDecimalScore score = scoreCalculator.calculateScore(solution);
    List<PredictionOutput> predictionOutputs = model.predictAsync(List.of(input)).get();
    assertTrue(score.isFeasible());
    assertEquals(2, goal.size());
    // A single prediction is expected
    assertEquals(1, predictionOutputs.size());
    // Single prediction with two features
    assertEquals(2, predictionOutputs.get(0).getOutputs().size());
    assertEquals(0, score.getHardScore(0).compareTo(BigDecimal.ZERO));
    assertEquals(0, score.getHardScore(1).compareTo(BigDecimal.ZERO));
    assertEquals(0, score.getHardScore(2).compareTo(BigDecimal.ZERO));
    assertEquals(0, score.getSoftScore(0).compareTo(BigDecimal.ZERO));
    assertEquals(0, score.getSoftScore(1).compareTo(BigDecimal.ZERO));
    assertEquals(3, score.getHardLevelsSize());
    assertEquals(2, score.getSoftLevelsSize());
}

Example 55 with Output

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

the class CounterfactualScoreCalculatorTest method DoubleDistanceSameValue.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2, 3, 4 })
void DoubleDistanceSameValue(int seed) {
    final Random random = new Random(seed);
    final double value = random.nextDouble();
    Feature x = FeatureFactory.newNumericalFeature("x", value);
    Feature y = FeatureFactory.newNumericalFeature("y", value);
    Output ox = outputFromFeature(x);
    Output oy = outputFromFeature(y);
    // Use a random threshold, mustn't make a difference
    final double distance = CounterFactualScoreCalculator.outputDistance(ox, oy, random.nextDouble());
    assertEquals(Type.NUMBER, ox.getType());
    assertEquals(0.0, distance);
}