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/* | |
* Copyright 2021 Red Hat, Inc. and/or its affiliates. | |
* | |
* Licensed under the Apache License, Version 2.0 (the "License"); | |
* you may not use this file except in compliance with the License. | |
* You may obtain a copy of the License at | |
* | |
* http://www.apache.org/licenses/LICENSE-2.0 | |
* | |
* Unless required by applicable law or agreed to in writing, software | |
* distributed under the License is distributed on an "AS IS" BASIS, | |
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
* See the License for the specific language governing permissions and | |
* limitations under the License. | |
*/ | |
package org.kie.kogito.explainability.explainability.integrationtests.pmml; | |
import java.nio.file.Paths; | |
import java.util.ArrayList; | |
import java.util.HashMap; | |
import java.util.List; | |
import java.util.Map; | |
import java.util.Random; | |
import java.util.concurrent.CompletableFuture; | |
import org.junit.jupiter.api.BeforeAll; | |
import org.junit.jupiter.api.Test; | |
import org.kie.api.pmml.PMML4Result; | |
import org.kie.api.pmml.PMMLRequestData; | |
import org.kie.kogito.explainability.Config; | |
import org.kie.kogito.explainability.local.LocalExplainer; | |
import org.kie.kogito.explainability.local.lime.LimeExplainer; | |
import org.kie.kogito.explainability.model.DataDistribution; | |
import org.kie.kogito.explainability.model.Feature; | |
import org.kie.kogito.explainability.model.Output; | |
import org.kie.kogito.explainability.model.Prediction; | |
import org.kie.kogito.explainability.model.PredictionInput; | |
import org.kie.kogito.explainability.model.PredictionOutput; | |
import org.kie.kogito.explainability.model.PredictionProvider; | |
import org.kie.kogito.explainability.model.Saliency; | |
import org.kie.kogito.explainability.model.Type; | |
import org.kie.kogito.explainability.model.Value; | |
import org.kie.kogito.explainability.utils.DataUtils; | |
import org.kie.kogito.explainability.utils.ExplainabilityMetrics; | |
import org.kie.pmml.api.runtime.PMMLContext; | |
import org.kie.pmml.api.runtime.PMMLRuntime; | |
import org.kie.pmml.evaluator.core.PMMLContextImpl; | |
import static org.kie.kogito.pmml.utils.PMMLUtils.getPMMLRequestData; | |
import static org.kie.pmml.evaluator.assembler.factories.PMMLRuntimeFactoryInternal.getPMMLRuntime; | |
class MinimalNumericPMMLLimeBenchmarkTest { | |
private static final String MODEL_NAME = "RandomForestClassifier"; | |
private static PMMLRuntime minimalNumericRuntime; | |
@BeforeAll | |
static void setUpBefore() { | |
minimalNumericRuntime = getPMMLRuntime(Paths.get("/home/tteofili/dev/benchmark-models/minimal-numerical/models/model.pmml").toFile()); | |
} | |
@Test | |
void test() throws Exception { | |
Random random = new Random(); | |
random.setSeed(4); | |
List<Type> schema = new ArrayList<>(); | |
schema.add(Type.NUMBER); | |
schema.add(Type.NUMBER); | |
schema.add(Type.NUMBER); | |
schema.add(Type.NUMBER); | |
DataDistribution dataDistribution = DataUtils.readCSV(Paths.get("/home/tteofili/dev/benchmark-models/minimal-numerical/data/processed/inputs.csv"), schema, true); | |
PredictionProvider model = inputs -> CompletableFuture.supplyAsync(() -> { | |
List<PredictionOutput> outputs = new ArrayList<>(inputs.size()); | |
for (PredictionInput input : inputs) { | |
Map<String, Object> map = new HashMap<>(); | |
for (Feature f : input.getFeatures()) { | |
map.put(f.getName(), f.getValue().asNumber()); | |
} | |
final PMMLRequestData pmmlRequestData = getPMMLRequestData("RandomForestClassifier", map); | |
final PMMLContext pmmlContext = new PMMLContextImpl(pmmlRequestData); | |
PMML4Result pmml4Result = minimalNumericRuntime.evaluate(MODEL_NAME, pmmlContext); | |
Map<String, Object> resultVariables = pmml4Result.getResultVariables(); | |
String score = "" + resultVariables.get("probability_1"); | |
String approved = "" + resultVariables.get("predicted_Approved"); | |
PredictionOutput predictionOutput = new PredictionOutput(List.of( | |
new Output("predicted_Approved", Type.TEXT, new Value(approved), Double.parseDouble(score)))); | |
outputs.add(predictionOutput); | |
} | |
return outputs; | |
}); | |
LocalExplainer<Map<String, Saliency>> limeExplainer = new LimeExplainer(); | |
List<PredictionInput> samples = dataDistribution.getAllSamples(); | |
List<PredictionOutput> predictionOutputs = model.predictAsync(samples).get(Config.DEFAULT_ASYNC_TIMEOUT, Config.DEFAULT_ASYNC_TIMEUNIT); | |
List<Prediction> predictions = DataUtils.getPredictions(samples, predictionOutputs); | |
double meanImpactScore = 0; | |
for (Prediction prediction : predictions) { | |
Map<String, Saliency> saliencyMap = limeExplainer.explainAsync(prediction, model) | |
.get(Config.INSTANCE.getAsyncTimeout(), Config.INSTANCE.getAsyncTimeUnit()); | |
for (Saliency saliency : saliencyMap.values()) { | |
double v = ExplainabilityMetrics.impactScore(model, prediction, saliency.getTopFeatures(2)); | |
meanImpactScore += v; | |
} | |
} | |
System.out.println(meanImpactScore / (double) predictions.size()); | |
} | |
} |
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