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

Example 81 with PredictionProvider

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

the class CountingOptimizationStrategyTest method testMaybeOptimize.

@Test
void testMaybeOptimize() {
    LimeOptimizationService optimizationService = mock(LimeOptimizationService.class);
    CountingOptimizationStrategy strategy = new CountingOptimizationStrategy(10, optimizationService);
    List<Prediction> recordedPredictions = Collections.emptyList();
    PredictionProvider model = mock(PredictionProvider.class);
    LimeExplainer explaier = new LimeExplainer();
    LimeConfig config = new LimeConfig();
    assertThatCode(() -> strategy.maybeOptimize(recordedPredictions, model, explaier, config)).doesNotThrowAnyException();
}

Example 82 with PredictionProvider

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

the class TestUtils method getFixedOutputClassifier.

public static PredictionProvider getFixedOutputClassifier() {
    return inputs -> supplyAsync(() -> {
        List<PredictionOutput> outputs = new LinkedList<>();
        for (PredictionInput ignored : inputs) {
            Output output = new Output("class", Type.BOOLEAN, new Value(false), 1d);
            outputs.add(new PredictionOutput(List.of(output)));
        }
        return outputs;
    });
}

Example 83 with PredictionProvider

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

the class TestUtils method getSumSkipModel.

public static PredictionProvider getSumSkipModel(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-but" + skipFeatureIndex, Type.NUMBER, new Value(result), 1d)));
            predictionOutputs.add(predictionOutput);
        }
        return predictionOutputs;
    });
}

Example 84 with PredictionProvider

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

the class TestUtils method getSumSkipTwoOutputModel.

public static PredictionProvider getSumSkipTwoOutputModel(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();
                }
            }
            Output output0 = new Output("sum-but" + skipFeatureIndex, Type.NUMBER, new Value(result), 1d);
            Output output1 = new Output("sum-but" + skipFeatureIndex + "*2", Type.NUMBER, new Value(result * 2), 1d);
            PredictionOutput predictionOutput = new PredictionOutput(List.of(output0, output1));
            predictionOutputs.add(predictionOutput);
        }
        return predictionOutputs;
    });
}

Example 85 with PredictionProvider

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