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

Example 26 with Value

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

the class ConversionUtils method toTypedValue.

static TypedValue toTypedValue(Output output) {
    String name = output.getName();
    Type type = output.getType();
    Value value = output.getValue();
    return toTypedValue(name, type, value);
}

Example 27 with Value

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

the class LoanEligibilityDmnCounterfactualExplainerTest method testLoanEligibilityDMNExplanation.

@Test
void testLoanEligibilityDMNExplanation() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = getModel();
    final List<Output> goal = List.of(new Output("Is Enought?", Type.NUMBER, new Value(100), 0.0d), new Output("Eligibility", Type.TEXT, new Value("No"), 0.0d), new Output("Decide", Type.BOOLEAN, new Value(true), 0.0d));
    final TerminationConfig terminationConfig = new TerminationConfig().withScoreCalculationCountLimit(steps);
    final SolverConfig solverConfig = SolverConfigBuilder.builder().withTerminationConfig(terminationConfig).build();
    solverConfig.setRandomSeed(randomSeed);
    solverConfig.setEnvironmentMode(EnvironmentMode.REPRODUCIBLE);
    CounterfactualConfig config = new CounterfactualConfig();
    config.withSolverConfig(solverConfig);
    final CounterfactualExplainer explainer = new CounterfactualExplainer(config);
    PredictionInput input = getTestInput();
    PredictionOutput output = new PredictionOutput(goal);
    // test model
    List<PredictionOutput> predictionOutputs = model.predictAsync(List.of(input)).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    Prediction prediction = new CounterfactualPrediction(input, output, null, UUID.randomUUID(), null);
    CounterfactualResult counterfactualResult = explainer.explainAsync(prediction, model).get();
    List<Feature> cfFeatures = counterfactualResult.getEntities().stream().map(CounterfactualEntity::asFeature).collect(Collectors.toList());
    List<Feature> unflattened = CompositeFeatureUtils.unflattenFeatures(cfFeatures, input.getFeatures());
    List<PredictionOutput> outputs = model.predictAsync(List.of(new PredictionInput(unflattened))).get();
    assertTrue(counterfactualResult.isValid());
    final Output decideOutput = outputs.get(0).getOutputs().get(2);
    assertEquals("Decide", decideOutput.getName());
    assertTrue((Boolean) decideOutput.getValue().getUnderlyingObject());
}

Example 28 with Value

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

the class OpenNLPLimeExplainerTest method getModel.

private PredictionProvider getModel() throws IOException {
    InputStream is = getClass().getResourceAsStream("/opennlp/langdetect-183.bin");
    LanguageDetectorModel languageDetectorModel = new LanguageDetectorModel(is);
    LanguageDetector languageDetector = new LanguageDetectorME(languageDetectorModel);
    return inputs -> CompletableFuture.supplyAsync(() -> {
        List<PredictionOutput> results = new LinkedList<>();
        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;
    });
}

Example 29 with Value

use of org.kie.kogito.explainability.model.Value 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 30 with Value

use of org.kie.kogito.explainability.model.Value 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;
    });
}