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

Example 71 with Output

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

the class TestUtils method getLinearModel.

public static PredictionProvider getLinearModel(double[] weights) {
    return inputs -> supplyAsync(() -> {
        List<PredictionOutput> predictionOutputs = new LinkedList<>();
        for (PredictionInput predictionInput : inputs) {
            List<Feature> features = predictionInput.getFeatures();
            double result = 0;
            for (int i = 0; i < features.size(); i++) {
                result += features.get(i).getValue().asNumber() * weights[i];
            }
            PredictionOutput predictionOutput = new PredictionOutput(List.of(new Output("linear-sum", Type.NUMBER, new Value(result), 1d)));
            predictionOutputs.add(predictionOutput);
        }
        return predictionOutputs;
    });
}

Example 72 with Output

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

the class TestUtils method getEvenSumModel.

public static PredictionProvider getEvenSumModel(int skipFeatureIndex) {
    return inputs -> supplyAsync(() -> {
        List<PredictionOutput> predictionOutputs = new LinkedList<>();
        for (PredictionInput predictionInput : inputs) {
            List<Feature> features = predictionInput.getFeatures();
            double result = 0;
            for (int i = 0; i < features.size(); i++) {
                if (skipFeatureIndex != i) {
                    result += features.get(i).getValue().asNumber();
                }
            }
            PredictionOutput predictionOutput = new PredictionOutput(List.of(new Output("sum-even-but" + skipFeatureIndex, Type.BOOLEAN, new Value(((int) result) % 2 == 0), 1d)));
            predictionOutputs.add(predictionOutput);
        }
        return predictionOutputs;
    });
}

Example 73 with Output

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

the class TestUtils method getDummyTextClassifier.

public static PredictionProvider getDummyTextClassifier() {
    List<String> blackList = Arrays.asList("money", "$", "£", "bitcoin");
    return inputs -> supplyAsync(() -> {
        List<PredictionOutput> outputs = new LinkedList<>();
        for (PredictionInput input : inputs) {
            boolean spam = false;
            for (Feature f : input.getFeatures()) {
                if (!spam) {
                    String s = f.getValue().asString();
                    String[] words = s.split(" ");
                    for (String w : words) {
                        if (blackList.contains(w)) {
                            spam = true;
                            break;
                        }
                    }
                }
            }
            Output output = new Output("spam", Type.BOOLEAN, new Value(spam), 1d);
            outputs.add(new PredictionOutput(List.of(output)));
        }
        return outputs;
    });
}

Example 74 with Output

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

the class TestUtils method getFeaturePassModel.

public static PredictionProvider getFeaturePassModel(int featureIndex) {
    return inputs -> supplyAsync(() -> {
        List<PredictionOutput> predictionOutputs = new LinkedList<>();
        for (PredictionInput predictionInput : inputs) {
            List<Feature> features = predictionInput.getFeatures();
            Feature feature = features.get(featureIndex);
            PredictionOutput predictionOutput = new PredictionOutput(List.of(new Output("feature-" + featureIndex, feature.getType(), feature.getValue(), 1d)));
            predictionOutputs.add(predictionOutput);
        }
        return predictionOutputs;
    });
}

Example 75 with Output

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

the class TestUtils method getFeatureSkipModel.

/**
 * Test model which returns the inputs as outputs, except for a single specified feature
 *
 * @param featureIndex Index of the input feature to omit from output
 * @return A {@link PredictionProvider} model
 */
public static PredictionProvider getFeatureSkipModel(int featureIndex) {
    return inputs -> supplyAsync(() -> {
        List<PredictionOutput> predictionOutputs = new LinkedList<>();
        for (PredictionInput predictionInput : inputs) {
            List<Feature> features = predictionInput.getFeatures();
            List<Output> outputs = new ArrayList<>();
            for (int i = 0; i < features.size(); i++) {
                if (i != featureIndex) {
                    Feature feature = features.get(i);
                    outputs.add(new Output(feature.getName(), feature.getType(), feature.getValue(), 1.0));
                }
            }
            PredictionOutput predictionOutput = new PredictionOutput(outputs);
            predictionOutputs.add(predictionOutput);
        }
        return predictionOutputs;
    });
}