use of org.kie.kogito.explainability.local.lime.LimeExplainer in project kogito-apps by kiegroup.
the class PrequalificationDmnLimeExplainerTest method testExplanationWeightedStabilityWithOptimization.
@Test
void testExplanationWeightedStabilityWithOptimization() throws ExecutionException, InterruptedException, TimeoutException {
PredictionProvider model = getModel();
List<PredictionInput> samples = DmnTestUtils.randomPrequalificationInputs();
List<PredictionOutput> predictionOutputs = model.predictAsync(samples.subList(0, 10)).get();
List<Prediction> predictions = DataUtils.getPredictions(samples, predictionOutputs);
long seed = 0;
LimeConfigOptimizer limeConfigOptimizer = new LimeConfigOptimizer().withDeterministicExecution(true).withWeightedStability(0.4, 0.6).withSampling(false).withStepCountLimit(20);
Random random = new Random();
LimeConfig initialConfig = new LimeConfig().withSamples(10).withPerturbationContext(new PerturbationContext(seed, random, 1));
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, 0.5, 0.3));
}
use of org.kie.kogito.explainability.local.lime.LimeExplainer in project kogito-apps by kiegroup.
the class TrafficViolationDmnLimeExplainerTest method testExplanationWeightedStabilityWithOptimization.
@Test
void testExplanationWeightedStabilityWithOptimization() throws ExecutionException, InterruptedException, TimeoutException {
PredictionProvider model = getModel();
List<PredictionInput> samples = DmnTestUtils.randomTrafficViolationInputs();
List<PredictionOutput> predictionOutputs = model.predictAsync(samples.subList(0, 5)).get();
List<Prediction> predictions = DataUtils.getPredictions(samples, predictionOutputs);
long seed = 0;
Random random = new Random();
LimeConfigOptimizer limeConfigOptimizer = new LimeConfigOptimizer().withDeterministicExecution(true).withSampling(false).withWeightedStability(0.4, 0.6).withStepCountLimit(20);
PerturbationContext perturbationContext = new PerturbationContext(seed, random, 2);
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, 0.4, 0.6));
}
use of org.kie.kogito.explainability.local.lime.LimeExplainer in project kogito-apps by kiegroup.
the class TrafficViolationDmnLimeExplainerTest method testExplanationStabilityWithOptimization.
@Test
void testExplanationStabilityWithOptimization() throws ExecutionException, InterruptedException, TimeoutException {
PredictionProvider model = getModel();
List<PredictionInput> samples = DmnTestUtils.randomTrafficViolationInputs();
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).withSampling(false).withStepCountLimit(30);
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, 0.5, 0.5));
}
use of org.kie.kogito.explainability.local.lime.LimeExplainer in project kogito-apps by kiegroup.
the class OpenNLPLimeExplainerTest method testOpenNLPLangDetect.
@ParameterizedTest
@ValueSource(longs = { 0 })
void testOpenNLPLangDetect(long seed) throws Exception {
Random random = new Random();
LimeConfig limeConfig = new LimeConfig().withSamples(10).withPerturbationContext(new PerturbationContext(seed, random, 1));
LimeExplainer limeExplainer = new LimeExplainer(limeConfig);
PredictionProvider model = getModel();
Function<String, List<String>> tokenizer = getTokenizer();
PredictionInput testInput = getTestInput(tokenizer);
List<PredictionOutput> predictionOutputs = model.predictAsync(List.of(testInput)).get();
assertNotNull(predictionOutputs);
assertFalse(predictionOutputs.isEmpty());
PredictionOutput output = predictionOutputs.get(0);
assertNotNull(output);
assertNotNull(output.getOutputs());
assertEquals(1, output.getOutputs().size());
assertEquals("ita", output.getOutputs().get(0).getValue().asString());
assertEquals(0.03, output.getOutputs().get(0).getScore(), 1e-2);
Prediction prediction = new SimplePrediction(testInput, output);
Map<String, Saliency> saliencyMap = limeExplainer.explainAsync(prediction, model).get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit());
for (Saliency saliency : saliencyMap.values()) {
assertNotNull(saliency);
double i1 = ExplainabilityMetrics.impactScore(model, prediction, saliency.getPositiveFeatures(3));
assertEquals(1d, i1);
}
assertDoesNotThrow(() -> ValidationUtils.validateLocalSaliencyStability(model, prediction, limeExplainer, 2, 0.6, 0.6));
List<PredictionInput> inputs = getSamples(tokenizer);
String decision = "lang";
DataDistribution distribution = new PredictionInputsDataDistribution(inputs);
int k = 2;
int chunkSize = 2;
double f1 = ExplainabilityMetrics.getLocalSaliencyF1(decision, model, limeExplainer, distribution, k, chunkSize);
assertThat(f1).isBetween(0.5d, 1d);
}
use of org.kie.kogito.explainability.local.lime.LimeExplainer in project kogito-apps by kiegroup.
the class OpenNLPLimeExplainerTest method testExplanationWeightedStabilityWithOptimization.
@Test
void testExplanationWeightedStabilityWithOptimization() throws ExecutionException, InterruptedException, TimeoutException, IOException {
PredictionProvider model = getModel();
List<PredictionInput> samples = getSamples(getTokenizer());
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).withSampling(false).withStepCountLimit(30).withWeightedStability(0.4, 0.6);
Random random = new Random();
LimeConfig limeConfig = new LimeConfig().withSamples(10).withPerturbationContext(new PerturbationContext(seed, random, 1));
LimeConfig optimizedConfig = limeConfigOptimizer.optimize(limeConfig, predictions, model);
Assertions.assertThat(optimizedConfig).isNotSameAs(limeConfig);
LimeExplainer limeExplainer = new LimeExplainer(optimizedConfig);
PredictionInput testPredictionInput = getTestInput(getTokenizer());
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, 0.9, 0.8));
}
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