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

Example 56 with PredictionInput

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

the class PmmlRegressionCategoricalLimeExplainerTest method testExplanationStabilityWithOptimization.

@Disabled("See KOGITO-6154")
@Test
void testExplanationStabilityWithOptimization() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = getModel();
    List<PredictionInput> samples = getSamples();
    List<PredictionOutput> predictionOutputs = model.predictAsync(samples.subList(0, 5)).get();
    List<Prediction> predictions = DataUtils.getPredictions(samples, predictionOutputs);
    long seed = 0;
    LimeConfigOptimizer limeConfigOptimizer = new LimeConfigOptimizer().withDeterministicExecution(true);
    Random random = new Random();
    PerturbationContext perturbationContext = new PerturbationContext(seed, random, 1);
    LimeConfig initialConfig = new LimeConfig().withSamples(10).withPerturbationContext(perturbationContext);
    LimeConfig optimizedConfig = limeConfigOptimizer.optimize(initialConfig, predictions, model);
    assertThat(optimizedConfig).isNotSameAs(initialConfig);
    LimeExplainer limeExplainer = new LimeExplainer(optimizedConfig);
    PredictionInput testPredictionInput = getTestInput();
    List<PredictionOutput> testPredictionOutputs = model.predictAsync(List.of(testPredictionInput)).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    Prediction instance = new SimplePrediction(testPredictionInput, testPredictionOutputs.get(0));
    assertDoesNotThrow(() -> ValidationUtils.validateLocalSaliencyStability(model, instance, limeExplainer, 1, 0.6, 0.6));
}

Example 57 with PredictionInput

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

the class CounterfactualScoreCalculatorTest method testGoalSizeMatch.

/**
 * If the goal and the model's output is the same, the distances should all be zero.
 */
@Test
void testGoalSizeMatch() throws ExecutionException, InterruptedException {
    final CounterFactualScoreCalculator scoreCalculator = new CounterFactualScoreCalculator();
    PredictionProvider model = TestUtils.getFeatureSkipModel(0);
    List<Feature> features = new ArrayList<>();
    List<FeatureDomain> featureDomains = new ArrayList<>();
    List<Boolean> constraints = new ArrayList<>();
    // f-1
    features.add(FeatureFactory.newNumericalFeature("f-1", 1.0));
    featureDomains.add(NumericalFeatureDomain.create(0.0, 10.0));
    constraints.add(false);
    // f-2
    features.add(FeatureFactory.newNumericalFeature("f-2", 2.0));
    featureDomains.add(NumericalFeatureDomain.create(0.0, 10.0));
    constraints.add(false);
    // f-3
    features.add(FeatureFactory.newBooleanFeature("f-3", true));
    featureDomains.add(EmptyFeatureDomain.create());
    constraints.add(false);
    PredictionInput input = new PredictionInput(features);
    PredictionFeatureDomain domains = new PredictionFeatureDomain(featureDomains);
    List<CounterfactualEntity> entities = CounterfactualEntityFactory.createEntities(input);
    List<Output> goal = new ArrayList<>();
    goal.add(new Output("f-2", Type.NUMBER, new Value(2.0), 0.0));
    goal.add(new Output("f-3", Type.BOOLEAN, new Value(true), 0.0));
    final CounterfactualSolution solution = new CounterfactualSolution(entities, features, model, goal, UUID.randomUUID(), UUID.randomUUID(), 0.0);
    BendableBigDecimalScore score = scoreCalculator.calculateScore(solution);
    List<PredictionOutput> predictionOutputs = model.predictAsync(List.of(input)).get();
    assertTrue(score.isFeasible());
    assertEquals(2, goal.size());
    // A single prediction is expected
    assertEquals(1, predictionOutputs.size());
    // Single prediction with two features
    assertEquals(2, predictionOutputs.get(0).getOutputs().size());
    assertEquals(0, score.getHardScore(0).compareTo(BigDecimal.ZERO));
    assertEquals(0, score.getHardScore(1).compareTo(BigDecimal.ZERO));
    assertEquals(0, score.getHardScore(2).compareTo(BigDecimal.ZERO));
    assertEquals(0, score.getSoftScore(0).compareTo(BigDecimal.ZERO));
    assertEquals(0, score.getSoftScore(1).compareTo(BigDecimal.ZERO));
    assertEquals(3, score.getHardLevelsSize());
    assertEquals(2, score.getSoftLevelsSize());
}

Example 58 with PredictionInput

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

the class DatasetEncoderTest method testDatasetEncodingWithBooleanData.

@Test
void testDatasetEncodingWithBooleanData() {
    List<PredictionInput> perturbedInputs = new LinkedList<>();
    for (int i = 0; i < 10; i++) {
        List<Feature> inputFeatures = new LinkedList<>();
        for (int j = 0; j < 3; j++) {
            inputFeatures.add(TestUtils.getMockedFeature(Type.BOOLEAN, new Value(j % 2 == 0)));
        }
        perturbedInputs.add(new PredictionInput(inputFeatures));
    }
    List<Feature> features = new LinkedList<>();
    for (int i = 0; i < 3; i++) {
        features.add(TestUtils.getMockedFeature(Type.BOOLEAN, new Value(i % 2 == 0)));
    }
    PredictionInput originalInput = new PredictionInput(features);
    assertEncode(perturbedInputs, originalInput);
}

Example 59 with PredictionInput

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

the class DatasetEncoderTest method testEmptyDatasetEncoding.

@Test
void testEmptyDatasetEncoding() {
    List<PredictionInput> inputs = new LinkedList<>();
    List<Output> outputs = new LinkedList<>();
    List<Feature> features = new LinkedList<>();
    Output originalOutput = new Output("foo", Type.NUMBER, new Value(1), 1d);
    EncodingParams params = new EncodingParams(1, 0.1);
    DatasetEncoder datasetEncoder = new DatasetEncoder(inputs, outputs, features, originalOutput, params);
    Collection<Pair<double[], Double>> trainingSet = datasetEncoder.getEncodedTrainingSet();
    assertNotNull(trainingSet);
    assertTrue(trainingSet.isEmpty());
}

Example 60 with PredictionInput

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

the class DatasetEncoderTest method testDatasetEncodingWithBinaryData.

@Test
void testDatasetEncodingWithBinaryData() {
    List<PredictionInput> perturbedInputs = new LinkedList<>();
    for (int i = 0; i < 10; i++) {
        List<Feature> inputFeatures = new LinkedList<>();
        for (int j = 0; j < 3; j++) {
            ByteBuffer byteBuffer = ByteBuffer.wrap((i + "" + j).getBytes(Charset.defaultCharset()));
            inputFeatures.add(TestUtils.getMockedFeature(Type.BINARY, new Value(byteBuffer)));
        }
        perturbedInputs.add(new PredictionInput(inputFeatures));
    }
    List<Feature> features = new LinkedList<>();
    for (int i = 0; i < 3; i++) {
        ByteBuffer byteBuffer = ByteBuffer.wrap((i + "" + i).getBytes(Charset.defaultCharset()));
        features.add(TestUtils.getMockedFeature(Type.BINARY, new Value(byteBuffer)));
    }
    PredictionInput originalInput = new PredictionInput(features);
    assertEncode(perturbedInputs, originalInput);
}