Program: 33055
Project: 33055
Buggy source:
static void train(Config config) throws Exception {
  String archive=config.getString("String_Node_Str");
  int numIterations=config.getInt("String_Node_Str");
  int numLeaves=config.getInt("String_Node_Str");
  double learningRate=config.getDouble("String_Node_Str");
  int minDataPerLeaf=config.getInt("String_Node_Str");
  String modelName=config.getString("String_Node_Str");
  double featureSamplingRate=config.getDouble("String_Node_Str");
  double dataSamplingRate=config.getDouble("String_Node_Str");
  StopWatch stopWatch=new StopWatch();
  stopWatch.start();
  MultiLabelClfDataSet dataSet=loadTrainData(config);
  MultiLabelClfDataSet testDataSet=loadTestData(config);
  Set<String> featuresToUseOption=Arrays.stream(config.getString("String_Node_Str").split("String_Node_Str")).map(string -> string.trim()).collect(Collectors.toSet());
  FeatureMappers featureMappers=dataSet.getSetting().getFeatureMappers();
  Set<Integer> initialFeatures=new HashSet<>();
  for (  CategoricalFeatureMapper mapper : featureMappers.getCategoricalFeatureMappers()) {
    if (mapper.getSource().equalsIgnoreCase("String_Node_Str")) {
      for (int j=mapper.getStart(); j < mapper.getEnd(); j++) {
        initialFeatures.add(j);
      }
    }
  }
  for (  NumericalFeatureMapper mapper : featureMappers.getNumericalFeatureMappers()) {
    if (mapper.getSource().equalsIgnoreCase("String_Node_Str")) {
      initialFeatures.add(mapper.getFeatureIndex());
    }
  }
  Set<Integer> unigramFeatures=new HashSet<>();
  for (  NumericalFeatureMapper mapper : featureMappers.getNumericalFeatureMappers()) {
    if (mapper.getSource().equalsIgnoreCase("String_Node_Str") && mapper.getFeatureName().split("String_Node_Str").length == 1) {
      unigramFeatures.add(mapper.getFeatureIndex());
    }
  }
  Set<Integer> ngramFeatures=new HashSet<>();
  for (  NumericalFeatureMapper mapper : featureMappers.getNumericalFeatureMappers()) {
    if (mapper.getSource().equalsIgnoreCase("String_Node_Str") && mapper.getFeatureName().split("String_Node_Str").length > 1) {
      ngramFeatures.add(mapper.getFeatureIndex());
    }
  }
  if (initialFeatures.size() + unigramFeatures.size() + ngramFeatures.size() != dataSet.getNumFeatures()) {
    throw new RuntimeException("String_Node_Str");
  }
  Set<Integer> featuresToUse=new HashSet<>();
  if (featuresToUseOption.contains("String_Node_Str")) {
    featuresToUse.addAll(initialFeatures);
  }
  if (featuresToUseOption.contains("String_Node_Str")) {
    featuresToUse.addAll(unigramFeatures);
  }
  if (featuresToUseOption.contains("String_Node_Str")) {
    featuresToUse.addAll(ngramFeatures);
  }
  int[] activeFeatures=featuresToUse.stream().mapToInt(i -> i).toArray();
  int numClasses=dataSet.getNumClasses();
  System.out.println("String_Node_Str" + numClasses);
  IMLGBConfig imlgbConfig=new IMLGBConfig.Builder(dataSet).dataSamplingRate(dataSamplingRate).featureSamplingRate(featureSamplingRate).learningRate(learningRate).minDataPerLeaf(minDataPerLeaf).numLeaves(numLeaves).numSplitIntervals(config.getInt("String_Node_Str")).build();
  IMLGradientBoosting boosting=new IMLGradientBoosting(numClasses);
  List<MultiLabel> assignments=DataSetUtil.gatherLabels(dataSet);
  boosting.setAssignments(assignments);
  boosting.setPriorProbs(dataSet);
  boosting.setTrainConfig(imlgbConfig);
  boosting.setActiveFeatures(activeFeatures);
  for (int i=0; i < numIterations; i++) {
    System.out.println("String_Node_Str" + i);
    boosting.boostOneRound();
    if (config.getBoolean("String_Node_Str")) {
      System.out.println("String_Node_Str" + Accuracy.accuracy(boosting,dataSet));
      System.out.println("String_Node_Str" + Overlap.overlap(boosting,dataSet));
      System.out.println("String_Node_Str" + Accuracy.accuracy(boosting,testDataSet));
      System.out.println("String_Node_Str" + Overlap.overlap(boosting,testDataSet));
    }
  }
  File serializedModel=new File(archive,modelName);
  boosting.serialize(serializedModel);
  System.out.println(stopWatch);
}

Fixed version (ground truth):
static void train(Config config) throws Exception {
  String archive=config.getString("String_Node_Str");
  int numIterations=config.getInt("String_Node_Str");
  int numLeaves=config.getInt("String_Node_Str");
  double learningRate=config.getDouble("String_Node_Str");
  int minDataPerLeaf=config.getInt("String_Node_Str");
  String modelName=config.getString("String_Node_Str");
  double featureSamplingRate=config.getDouble("String_Node_Str");
  double dataSamplingRate=config.getDouble("String_Node_Str");
  StopWatch stopWatch=new StopWatch();
  stopWatch.start();
  MultiLabelClfDataSet dataSet=loadTrainData(config);
  MultiLabelClfDataSet testDataSet=loadTestData(config);
  Set<String> featuresToUseOption=Arrays.stream(config.getString("String_Node_Str").split("String_Node_Str")).map(string -> string.trim()).collect(Collectors.toSet());
  FeatureMappers featureMappers=dataSet.getSetting().getFeatureMappers();
  Set<Integer> initialFeatures=new HashSet<>();
  for (  CategoricalFeatureMapper mapper : featureMappers.getCategoricalFeatureMappers()) {
    if (mapper.getSource().equalsIgnoreCase("String_Node_Str")) {
      for (int j=mapper.getStart(); j <= mapper.getEnd(); j++) {
        initialFeatures.add(j);
      }
    }
  }
  for (  NumericalFeatureMapper mapper : featureMappers.getNumericalFeatureMappers()) {
    if (mapper.getSource().equalsIgnoreCase("String_Node_Str")) {
      initialFeatures.add(mapper.getFeatureIndex());
    }
  }
  Set<Integer> unigramFeatures=new HashSet<>();
  for (  NumericalFeatureMapper mapper : featureMappers.getNumericalFeatureMappers()) {
    if (mapper.getSource().equalsIgnoreCase("String_Node_Str") && mapper.getFeatureName().split("String_Node_Str").length == 1) {
      unigramFeatures.add(mapper.getFeatureIndex());
    }
  }
  Set<Integer> ngramFeatures=new HashSet<>();
  for (  NumericalFeatureMapper mapper : featureMappers.getNumericalFeatureMappers()) {
    if (mapper.getSource().equalsIgnoreCase("String_Node_Str") && mapper.getFeatureName().split("String_Node_Str").length > 1) {
      ngramFeatures.add(mapper.getFeatureIndex());
    }
  }
  if (initialFeatures.size() + unigramFeatures.size() + ngramFeatures.size() != dataSet.getNumFeatures()) {
    throw new RuntimeException("String_Node_Str");
  }
  Set<Integer> featuresToUse=new HashSet<>();
  if (featuresToUseOption.contains("String_Node_Str")) {
    featuresToUse.addAll(initialFeatures);
  }
  if (featuresToUseOption.contains("String_Node_Str")) {
    featuresToUse.addAll(unigramFeatures);
  }
  if (featuresToUseOption.contains("String_Node_Str")) {
    featuresToUse.addAll(ngramFeatures);
  }
  int[] activeFeatures=featuresToUse.stream().mapToInt(i -> i).toArray();
  int numClasses=dataSet.getNumClasses();
  System.out.println("String_Node_Str" + numClasses);
  IMLGBConfig imlgbConfig=new IMLGBConfig.Builder(dataSet).dataSamplingRate(dataSamplingRate).featureSamplingRate(featureSamplingRate).learningRate(learningRate).minDataPerLeaf(minDataPerLeaf).numLeaves(numLeaves).numSplitIntervals(config.getInt("String_Node_Str")).build();
  IMLGradientBoosting boosting=new IMLGradientBoosting(numClasses);
  List<MultiLabel> assignments=DataSetUtil.gatherLabels(dataSet);
  boosting.setAssignments(assignments);
  boosting.setPriorProbs(dataSet);
  boosting.setTrainConfig(imlgbConfig);
  boosting.setActiveFeatures(activeFeatures);
  for (int i=0; i < numIterations; i++) {
    System.out.println("String_Node_Str" + i);
    boosting.boostOneRound();
    if (config.getBoolean("String_Node_Str")) {
      System.out.println("String_Node_Str" + Accuracy.accuracy(boosting,dataSet));
      System.out.println("String_Node_Str" + Overlap.overlap(boosting,dataSet));
      System.out.println("String_Node_Str" + Accuracy.accuracy(boosting,testDataSet));
      System.out.println("String_Node_Str" + Overlap.overlap(boosting,testDataSet));
    }
  }
  File serializedModel=new File(archive,modelName);
  boosting.serialize(serializedModel);
  System.out.println(stopWatch);
}