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

Example 36 with Value

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

the class FairnessMetricsTest method testGroupDIRTextClassifier.

@Test
void testGroupDIRTextClassifier() throws ExecutionException, InterruptedException {
    List<PredictionInput> testInputs = getTestInputs();
    PredictionProvider model = TestUtils.getDummyTextClassifier();
    Predicate<PredictionInput> selector = predictionInput -> DataUtils.textify(predictionInput).contains("please");
    Output output = new Output("spam", Type.BOOLEAN, new Value(false), 1.0);
    double dir = FairnessMetrics.groupDisparateImpactRatio(selector, testInputs, model, output);
    assertThat(dir).isPositive();
}

Example 37 with Value

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

the class MatrixUtilsExtensionsTest method testPOListCreation.

// test creation of matrix from list of PredictionOutputs
@Test
void testPOListCreation() {
    // use the mat 3x5 as our list of prediction outputs
    List<PredictionOutput> ps = new ArrayList<>();
    for (int i = 0; i < 3; i++) {
        List<Output> os = new ArrayList<>();
        for (int j = 0; j < 5; j++) {
            Value v = new Value(mat3X5[i][j]);
            os.add(new Output("o", Type.NUMBER, v, 0.0));
        }
        ps.add(new PredictionOutput(os));
    }
    RealMatrix converted = MatrixUtilsExtensions.matrixFromPredictionOutput(ps);
    assertArrayEquals(mat3X5, converted.getData());
}

Example 38 with Value

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

the class DecisionModelWrapper method predictAsync.

@Override
public CompletableFuture<List<PredictionOutput>> predictAsync(List<PredictionInput> inputs) {
    List<PredictionOutput> predictionOutputs = new LinkedList<>();
    for (PredictionInput input : inputs) {
        Map<String, Object> contextVariables = toMap(input.getFeatures());
        final DMNContext context = decisionModel.newContext(contextVariables);
        DMNResult dmnResult = decisionModel.evaluateAll(context);
        List<Output> outputs = new LinkedList<>();
        for (DMNDecisionResult decisionResult : dmnResult.getDecisionResults()) {
            String decisionName = decisionResult.getDecisionName();
            if (!skippedDecisions.contains(decisionName)) {
                Object result = decisionResult.getResult();
                Value value = new Value(result);
                Type type;
                if (result == null) {
                    type = Type.TEXT;
                } else {
                    if (result instanceof Boolean) {
                        type = Type.BOOLEAN;
                    } else if (result instanceof String) {
                        type = Type.TEXT;
                    } else {
                        type = Type.NUMBER;
                    }
                }
                Output output = new Output(decisionName, type, value, 1d);
                outputs.add(output);
            }
        }
        PredictionOutput predictionOutput = new PredictionOutput(outputs);
        predictionOutputs.add(predictionOutput);
    }
    return completedFuture(predictionOutputs);
}

Example 39 with Value

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

the class ShapResultsTest method buildShapResults.

ShapResults buildShapResults(int nOutputs, int nFeatures, int scalar1, int scalar2) {
    Saliency[] saliencies = new Saliency[nOutputs];
    for (int i = 0; i < nOutputs; i++) {
        List<FeatureImportance> fis = new ArrayList<>();
        for (int j = 0; j < nFeatures; j++) {
            fis.add(new FeatureImportance(new Feature("f" + String.valueOf(j), Type.NUMBER, new Value(j)), i * j * scalar1));
        }
        saliencies[i] = new Saliency(new Output("o" + String.valueOf(i), Type.NUMBER, new Value(i), 1.0), fis);
    }
    RealVector fnull = MatrixUtils.createRealVector(new double[nOutputs]);
    fnull.mapAddToSelf(scalar2);
    return new ShapResults(saliencies, fnull);
}

Example 40 with Value

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

the class DataUtilsTest method toCSV.

@Test
void toCSV() {
    Feature feature = mock(Feature.class);
    when(feature.getName()).thenReturn("feature-1");
    Output output = mock(Output.class);
    when(output.getName()).thenReturn("decision-1");
    List<Value> x = new ArrayList<>();
    x.add(new Value(1));
    x.add(new Value(2));
    x.add(new Value(3));
    List<Value> y = new ArrayList<>();
    y.add(new Value(4));
    y.add(new Value(5));
    y.add(new Value(4));
    PartialDependenceGraph partialDependenceGraph = new PartialDependenceGraph(feature, output, x, y);
    assertDoesNotThrow(() -> DataUtils.toCSV(partialDependenceGraph, Paths.get("target/test-pdp.csv")));
}