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

Example 31 with Output

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

the class CounterfactualScoreCalculatorTest method DoubleDistanceDifferentValueThreshold.

@ParameterizedTest
@ValueSource(ints = { 0, 1, 2, 3, 4 })
void DoubleDistanceDifferentValueThreshold(int seed) {
    final Random random = new Random(seed);
    double value = random.nextDouble() * 100.0;
    Feature x = FeatureFactory.newNumericalFeature("x", value);
    Feature y = FeatureFactory.newNumericalFeature("y", value + 100.0);
    Output ox = outputFromFeature(x);
    Output oy = outputFromFeature(y);
    double distance = CounterFactualScoreCalculator.outputDistance(ox, oy, 0.25);
    assertEquals(Type.NUMBER, ox.getType());
    assertEquals(Type.NUMBER, oy.getType());
    assertTrue(distance * distance > 0);
    y = FeatureFactory.newNumericalFeature("y", value - 100);
    oy = outputFromFeature(y);
    distance = CounterFactualScoreCalculator.outputDistance(ox, oy, 0.25);
    assertTrue(distance * distance > 0);
}

Example 32 with Output

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

the class CounterfactualScoreCalculatorTest method binaryDistanceSameValue.

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

Example 33 with Output

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

the class ExplainabilityMetrics method getLocalSaliencyRecall.

/**
 * Evaluate the recall of a local saliency explainer on a given model.
 * Get the predictions having outputs with the highest score for the given decision and pair them with predictions
 * whose outputs have the lowest score for the same decision.
 * Get the top k (most important) features (according to the saliency) for the most important outputs and
 * "paste" them on each paired input corresponding to an output with low score (for the target decision).
 * Perform prediction on the "masked" input, if the output on the masked input is equals to the output for the
 * input the mask features were take from, that's considered a true positive, otherwise it's a false positive.
 * see Section 3.2.1 of https://openreview.net/attachment?id=B1xBAA4FwH&name=original_pdf
 *
 * @param outputName decision to evaluate recall for
 * @param predictionProvider the prediction provider to test
 * @param localExplainer the explainer to evaluate
 * @param dataDistribution the data distribution used to obtain inputs for evaluation
 * @param k the no. of features to extract
 * @param chunkSize the size of the chunk of predictions to use for evaluation
 * @return the saliency recall
 */
public static double getLocalSaliencyRecall(String outputName, PredictionProvider predictionProvider, LocalExplainer<Map<String, Saliency>> localExplainer, DataDistribution dataDistribution, int k, int chunkSize) throws InterruptedException, ExecutionException, TimeoutException {
    // get all samples from the data distribution
    List<Prediction> sorted = DataUtils.getScoreSortedPredictions(outputName, predictionProvider, dataDistribution);
    // get the top and bottom 'chunkSize' predictions
    List<Prediction> topChunk = new ArrayList<>(sorted.subList(0, chunkSize));
    List<Prediction> bottomChunk = new ArrayList<>(sorted.subList(sorted.size() - chunkSize, sorted.size()));
    double truePositives = 0;
    double falseNegatives = 0;
    int currentChunk = 0;
    // input, then feed the model with this masked input and check the output is equals to the top scored one.
    for (Prediction prediction : topChunk) {
        Optional<Output> optionalOutput = prediction.getOutput().getByName(outputName);
        if (optionalOutput.isPresent()) {
            Output output = optionalOutput.get();
            Map<String, Saliency> stringSaliencyMap = localExplainer.explainAsync(prediction, predictionProvider).get(Config.DEFAULT_ASYNC_TIMEOUT, Config.DEFAULT_ASYNC_TIMEUNIT);
            if (stringSaliencyMap.containsKey(outputName)) {
                Saliency saliency = stringSaliencyMap.get(outputName);
                List<FeatureImportance> topFeatures = saliency.getPerFeatureImportance().stream().sorted((f1, f2) -> Double.compare(f2.getScore(), f1.getScore())).limit(k).collect(Collectors.toList());
                PredictionInput input = bottomChunk.get(currentChunk).getInput();
                PredictionInput maskedInput = maskInput(topFeatures, input);
                List<PredictionOutput> predictionOutputList = predictionProvider.predictAsync(List.of(maskedInput)).get(Config.DEFAULT_ASYNC_TIMEOUT, Config.DEFAULT_ASYNC_TIMEUNIT);
                if (!predictionOutputList.isEmpty()) {
                    PredictionOutput predictionOutput = predictionOutputList.get(0);
                    Optional<Output> optionalNewOutput = predictionOutput.getByName(outputName);
                    if (optionalNewOutput.isPresent()) {
                        Output newOutput = optionalOutput.get();
                        if (output.getValue().equals(newOutput.getValue())) {
                            truePositives++;
                        } else {
                            falseNegatives++;
                        }
                    }
                }
                currentChunk++;
            }
        }
    }
    if ((truePositives + falseNegatives) > 0) {
        return truePositives / (truePositives + falseNegatives);
    } else {
        // if topChunk is empty or the target output (by name) is not an output of the model.
        return Double.NaN;
    }
}

Example 34 with Output

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

the class ExplainabilityMetrics method getLocalSaliencyPrecision.

/**
 * Evaluate the precision of a local saliency explainer on a given model.
 * Get the predictions having outputs with the lowest score for the given decision and pair them with predictions
 * whose outputs have the highest score for the same decision.
 * Get the bottom k (less important) features (according to the saliency) for the less important outputs and
 * "paste" them on each paired input corresponding to an output with high score (for the target decision).
 * Perform prediction on the "masked" input, if the output changes that's considered a false negative, otherwise
 * it's a true positive.
 * see Section 3.2.1 of https://openreview.net/attachment?id=B1xBAA4FwH&name=original_pdf
 *
 * @param outputName decision to evaluate recall for
 * @param predictionProvider the prediction provider to test
 * @param localExplainer the explainer to evaluate
 * @param dataDistribution the data distribution used to obtain inputs for evaluation
 * @param k the no. of features to extract
 * @param chunkSize the size of the chunk of predictions to use for evaluation
 * @return the saliency precision
 */
public static double getLocalSaliencyPrecision(String outputName, PredictionProvider predictionProvider, LocalExplainer<Map<String, Saliency>> localExplainer, DataDistribution dataDistribution, int k, int chunkSize) throws InterruptedException, ExecutionException, TimeoutException {
    List<Prediction> sorted = DataUtils.getScoreSortedPredictions(outputName, predictionProvider, dataDistribution);
    // get the top and bottom 'chunkSize' predictions
    List<Prediction> topChunk = new ArrayList<>(sorted.subList(0, chunkSize));
    List<Prediction> bottomChunk = new ArrayList<>(sorted.subList(sorted.size() - chunkSize, sorted.size()));
    double truePositives = 0;
    double falsePositives = 0;
    int currentChunk = 0;
    for (Prediction prediction : bottomChunk) {
        Map<String, Saliency> stringSaliencyMap = localExplainer.explainAsync(prediction, predictionProvider).get(Config.DEFAULT_ASYNC_TIMEOUT, Config.DEFAULT_ASYNC_TIMEUNIT);
        if (stringSaliencyMap.containsKey(outputName)) {
            Saliency saliency = stringSaliencyMap.get(outputName);
            List<FeatureImportance> topFeatures = saliency.getPerFeatureImportance().stream().sorted(Comparator.comparingDouble(FeatureImportance::getScore)).limit(k).collect(Collectors.toList());
            Prediction topPrediction = topChunk.get(currentChunk);
            PredictionInput input = topPrediction.getInput();
            PredictionInput maskedInput = maskInput(topFeatures, input);
            List<PredictionOutput> predictionOutputList = predictionProvider.predictAsync(List.of(maskedInput)).get(Config.DEFAULT_ASYNC_TIMEOUT, Config.DEFAULT_ASYNC_TIMEUNIT);
            if (!predictionOutputList.isEmpty()) {
                PredictionOutput predictionOutput = predictionOutputList.get(0);
                Optional<Output> newOptionalOutput = predictionOutput.getByName(outputName);
                if (newOptionalOutput.isPresent()) {
                    Output newOutput = newOptionalOutput.get();
                    Optional<Output> optionalOutput = topPrediction.getOutput().getByName(outputName);
                    if (optionalOutput.isPresent()) {
                        Output output = optionalOutput.get();
                        if (output.getValue().equals(newOutput.getValue())) {
                            truePositives++;
                        } else {
                            falsePositives++;
                        }
                    }
                }
            }
            currentChunk++;
        }
    }
    if ((truePositives + falsePositives) > 0) {
        return truePositives / (truePositives + falsePositives);
    } else {
        // if bottomChunk is empty or the target output (by name) is not an output of the model.
        return Double.NaN;
    }
}

Example 35 with Output

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

the class FairnessMetrics method individualConsistency.

/**
 * Calculate individual fairness in terms of consistency of predictions across similar inputs.
 *
 * @param proximityFunction a function that finds the top k similar inputs, given a reference input and a list of inputs
 * @param samples a list of inputs to be tested for consistency
 * @param predictionProvider the model under inspection
 * @return the consistency measure
 * @throws ExecutionException if any error occurs during model prediction
 * @throws InterruptedException if timeout or other interruption issues occur during model prediction
 */
public static double individualConsistency(BiFunction<PredictionInput, List<PredictionInput>, List<PredictionInput>> proximityFunction, List<PredictionInput> samples, PredictionProvider predictionProvider) throws ExecutionException, InterruptedException {
    double consistency = 1;
    for (PredictionInput input : samples) {
        List<PredictionOutput> predictionOutputs = predictionProvider.predictAsync(List.of(input)).get();
        PredictionOutput predictionOutput = predictionOutputs.get(0);
        List<PredictionInput> neighbors = proximityFunction.apply(input, samples);
        List<PredictionOutput> neighborsOutputs = predictionProvider.predictAsync(neighbors).get();
        for (Output output : predictionOutput.getOutputs()) {
            Value originalValue = output.getValue();
            for (PredictionOutput neighborOutput : neighborsOutputs) {
                Output currentOutput = neighborOutput.getByName(output.getName()).orElse(null);
                if (currentOutput != null && !originalValue.equals(currentOutput.getValue())) {
                    consistency -= 1f / (neighbors.size() * predictionOutput.getOutputs().size() * samples.size());
                }
            }
        }
    }
    return consistency;
}