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

Example 46 with Prediction

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

the class ComplexEligibilityDmnCounterfactualExplainerTest method testDMNValidCounterfactualExplanation.

@Test
void testDMNValidCounterfactualExplanation() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = getModel();
    final List<Output> goal = generateGoal(true, true, 0.6);
    List<Feature> features = new LinkedList<>();
    features.add(FeatureFactory.newNumericalFeature("age", 40));
    features.add(FeatureFactory.newBooleanFeature("hasReferral", true));
    features.add(FeatureFactory.newNumericalFeature("monthlySalary", 500, NumericalFeatureDomain.create(10, 10_000)));
    final TerminationConfig terminationConfig = new TerminationConfig().withScoreCalculationCountLimit(10_000L);
    // for the purpose of this test, only a few steps are necessary
    final SolverConfig solverConfig = SolverConfigBuilder.builder().withTerminationConfig(terminationConfig).build();
    solverConfig.setRandomSeed((long) 23);
    solverConfig.setEnvironmentMode(EnvironmentMode.REPRODUCIBLE);
    final CounterfactualConfig counterfactualConfig = new CounterfactualConfig().withSolverConfig(solverConfig).withGoalThreshold(0.01);
    final CounterfactualExplainer counterfactualExplainer = new CounterfactualExplainer(counterfactualConfig);
    PredictionInput input = new PredictionInput(features);
    PredictionOutput output = new PredictionOutput(goal);
    Prediction prediction = new CounterfactualPrediction(input, output, null, UUID.randomUUID(), 60L);
    final CounterfactualResult counterfactualResult = counterfactualExplainer.explainAsync(prediction, model).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
    List<Output> cfOutputs = counterfactualResult.getOutput().get(0).getOutputs();
    assertTrue(counterfactualResult.isValid());
    assertEquals("inputsAreValid", cfOutputs.get(0).getName());
    assertTrue((Boolean) cfOutputs.get(0).getValue().getUnderlyingObject());
    assertEquals("canRequestLoan", cfOutputs.get(1).getName());
    assertTrue((Boolean) cfOutputs.get(1).getValue().getUnderlyingObject());
    assertEquals("my-scoring-function", cfOutputs.get(2).getName());
    assertEquals(0.6, ((BigDecimal) cfOutputs.get(2).getValue().getUnderlyingObject()).doubleValue(), 0.05);
    List<CounterfactualEntity> entities = counterfactualResult.getEntities();
    assertEquals("age", entities.get(0).asFeature().getName());
    assertEquals(40, entities.get(0).asFeature().getValue().asNumber());
    assertEquals("hasReferral", entities.get(1).asFeature().getName());
    assertTrue((Boolean) entities.get(1).asFeature().getValue().getUnderlyingObject());
    assertEquals("monthlySalary", entities.get(2).asFeature().getName());
    assertTrue(entities.get(2).asFeature().getValue().asNumber() > 6000);
}

Example 47 with Prediction

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

the class LimeConfigOptimizerTest method assertConfigOptimized.

private void assertConfigOptimized(LimeConfigOptimizer limeConfigOptimizer) throws InterruptedException, java.util.concurrent.ExecutionException {
    LimeConfig initialConfig = new LimeConfig().withSamples(10);
    PredictionProvider model = TestUtils.getSumSkipModel(1);
    Random random = new Random();
    random.setSeed(4);
    DataDistribution dataDistribution = DataUtils.generateRandomDataDistribution(5, 100, random);
    List<PredictionInput> samples = dataDistribution.sample(10);
    List<PredictionOutput> predictionOutputs = model.predictAsync(samples).get();
    List<Prediction> predictions = DataUtils.getPredictions(samples, predictionOutputs);
    LimeConfig optimizedConfig = limeConfigOptimizer.optimize(initialConfig, predictions, model);
    assertThat(optimizedConfig).isNotNull();
    Assertions.assertThat(optimizedConfig).isNotSameAs(initialConfig);
}

Example 48 with Prediction

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

the class LimeImpactScoreCalculatorTest method testNonZeroScore.

@Test
void testNonZeroScore() throws ExecutionException, InterruptedException, TimeoutException {
    PredictionProvider model = TestUtils.getDummyTextClassifier();
    LimeImpactScoreCalculator scoreCalculator = new LimeImpactScoreCalculator();
    LimeConfig config = new LimeConfig();
    List<Feature> features = List.of(FeatureFactory.newFulltextFeature("text", "money so they say is the root of all evil today"));
    PredictionInput input = new PredictionInput(features);
    List<PredictionOutput> predictionOutputs = model.predictAsync(List.of(input)).get(Config.DEFAULT_ASYNC_TIMEOUT, Config.DEFAULT_ASYNC_TIMEUNIT);
    assertThat(predictionOutputs).isNotNull();
    assertThat(predictionOutputs.size()).isEqualTo(1);
    PredictionOutput output = predictionOutputs.get(0);
    Prediction prediction = new SimplePrediction(input, output);
    List<Prediction> predictions = List.of(prediction);
    List<LimeConfigEntity> entities = LimeConfigEntityFactory.createEncodingEntities(config);
    LimeConfigSolution solution = new LimeConfigSolution(config, predictions, entities, model);
    SimpleBigDecimalScore score = scoreCalculator.calculateScore(solution);
    assertThat(score).isNotNull();
    assertThat(score.getScore()).isNotNull().isNotEqualTo(BigDecimal.valueOf(0));
}

Example 49 with Prediction

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

the class LimeStabilityScoreCalculatorTest method testScoreWithEmptyPredictions.

@Test
void testScoreWithEmptyPredictions() {
    LimeStabilityScoreCalculator scoreCalculator = new LimeStabilityScoreCalculator();
    LimeConfig config = new LimeConfig();
    List<Prediction> predictions = Collections.emptyList();
    List<LimeConfigEntity> entities = Collections.emptyList();
    PredictionProvider model = TestUtils.getDummyTextClassifier();
    LimeConfigSolution solution = new LimeConfigSolution(config, predictions, entities, model);
    SimpleBigDecimalScore score = scoreCalculator.calculateScore(solution);
    assertThat(score).isNotNull();
    assertThat(score.getScore()).isNotNull();
    assertThat(score.getScore()).isEqualTo(BigDecimal.valueOf(0));
}

Example 50 with Prediction

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

the class RecordingLimeExplainerTest method testRecordedPredictions.

@Test
void testRecordedPredictions() {
    RecordingLimeExplainer recordingLimeExplainer = new RecordingLimeExplainer(10);
    List<Prediction> allPredictions = new ArrayList<>();
    PredictionProvider model = mock(PredictionProvider.class);
    for (int i = 0; i < 15; i++) {
        Prediction prediction = mock(Prediction.class);
        allPredictions.add(prediction);
        try {
            recordingLimeExplainer.explainAsync(prediction, model).get(Config.DEFAULT_ASYNC_TIMEOUT, Config.DEFAULT_ASYNC_TIMEUNIT);
        } catch (Exception e) {
        // ignored for the sake of the test
        }
    }
    assertThat(allPredictions).hasSize(15);
    List<Prediction> recordedPredictions = recordingLimeExplainer.getRecordedPredictions();
    assertThat(recordedPredictions).hasSize(10);
    // only the last 10 predictions are kept
    assertThat(allPredictions.subList(5, 15)).isEqualTo(recordedPredictions);
}