private List runAlgorithm()

in ruta-ep-textruler/src/main/java/org/apache/uima/ruta/textruler/learner/trabal/TrabalLearner.java [500:592]
91 lines of code
19 McCabe index (conditional complexity)

  private List<TrabalRule> runAlgorithm(Map<String, List<AnnotationError>> errorGrps) {
    removeBasics();
    inducedRules.clear();

    List<TrabalRule> rules = new ArrayList<TrabalRule>();
    bestRulesForStatus.clear();
    int i = 1;
    for (List<AnnotationError> each : errorGrps.values()) {
      if (shouldAbort()) {
        break;
      }
      Collections.sort(each);
      String status = each.get(0).toString();
      sendStatusUpdateToDelegate("Creating basic rules: " + status,
              TextRulerLearnerState.ML_RUNNING, false);
      List<TrabalRule> basicRules = removeDuplicateRules(createBasicRules(each));
      if (basicRules.size() > maxNumberOfBasicRules) {
        basicRules = basicRules.subList(0, maxNumberOfBasicRules);
      }
      sendStatusUpdateToDelegate("Testing basic rules: " + status, TextRulerLearnerState.ML_RUNNING,
              false);
      basicRules = testTrabalRulesOnDocumentSet(basicRules, exampleDocuments, additionalDocuments,
              "basic rules (" + i + " of " + errorGrps.size() + ")");
      if (basicRules.size() > 0) {
        Collections.sort(basicRules, basicComparator);
        bestRulesForStatus.add(basicRules.get(0));
      }
      result = actualResult + getRuleStrings(bestRulesForStatus);
      sendStatusUpdateToDelegate("Testing basic rules: " + status, TextRulerLearnerState.ML_RUNNING,
              true);
      List<TrabalRule> learntRules = new ArrayList<TrabalRule>();
      for (TrabalRule rule : basicRules) {
        if (rule.getCoveringStatistics().getCoveredPositivesCount() > 0
                && rule.getCoveringStatistics().getCoveredNegativesCount() == 0) {
          learntRules.add(rule);
        }
      }
      // Collections.sort(learntRules, basicComparator);
      List<TrabalRule> enhancedRules = new ArrayList<TrabalRule>();
      int rank = 1;
      for (TrabalRule rule : basicRules) {
        if (rule.getCoveringStatistics().getCoveredPositivesCount() > 0
                && rule.getCoveringStatistics().getCoveredNegativesCount() > 0) {
          if (learntRules.size() > 0) {
            if (rule.getCoveringStatistics().getCoveredPositivesCount() > learntRules.get(0)
                    .getCoveringStatistics().getCoveredPositivesCount()) {
              rule.setRating(rank);
              enhancedRules.add(rule);
              rank++;
            }
          } else {
            rule.setRating(rank);
            enhancedRules.add(rule);
            rank++;
          }
        }
      }
      basicRules.clear();
      try {
        enhancedRules = enhanceRules(enhancedRules, maxNumberOfIterations, new RankedList(idf));
        Collections.sort(enhancedRules);
        if (enhancedRules.size() > maxNumberOfRules) {
          enhancedRules = enhancedRules.subList(0, maxNumberOfRules);
        }
      } catch (Exception e) {
        e.printStackTrace();
      }
      sendStatusUpdateToDelegate("Testing enhanced rules: " + status,
              TextRulerLearnerState.ML_RUNNING, false);
      enhancedRules = testTrabalRulesOnDocumentSet(enhancedRules, exampleDocuments,
              additionalDocuments, "enhanced rules (" + i + " of " + errorGrps.size() + ")");
      for (TrabalRule rule : enhancedRules) {
        if (rule.getErrorRate() <= maxErrorRate) {
          learntRules.add(rule);
        }
      }
      enhancedRules.clear();
      learntRules = removeDuplicateRules(learntRules);
      if (learntRules.size() > 0) {
        Collections.sort(learntRules, enhancedComparator);
        bestRulesForStatus.remove(bestRulesForStatus.size() - 1); // TODO
        bestRulesForStatus.add(learntRules.get(0));
        result = actualResult + getRuleStrings(bestRulesForStatus);
      }
      sendStatusUpdateToDelegate("Testing optimized rules: " + status,
              TextRulerLearnerState.ML_RUNNING, true);
      if (learntRules.size() > maxNumberOfRules)
        learntRules = learntRules.subList(0, maxNumberOfRules);
      rules.addAll(learntRules);
      i++;
    }
    return getBest(rules);
  }