コード例 #1
0
ファイル: ExternalBasicChart.java プロジェクト: noegodinho/AI
  private XYDataset createDataset(String metricName) {

    series = new XYSeriesCollection();

    // For each collection of evaluations
    for (int i = 0; i < evaluationsCollection.size(); i++) {

      if (!set.contains(i)) {

        List<AbstractEvaluation> evaluations = evaluationsCollection.get(i);
        XYSeries newXYSerie = new XYSeries(queryNames.get(i));

        int evalIndex = 0;

        for (AbstractEvaluation eval : evaluations) {
          if (evalIndex % reportFrecuency == 0) {
            newXYSerie.add(eval.getLabeledSetSize(), eval.getMetricValue(metricName));
          }
          ++evalIndex;
        }
        series.addSeries(newXYSerie);
      }
    }
    // The series that belongs to passive learning is the last
    if (passiveEvaluation != null) {

      series.addSeries(createPassiveLearningSerie(metricName, evaluationsCollection.get(0)));
    }
    return series;
  }
コード例 #2
0
ファイル: ExternalBasicChart.java プロジェクト: noegodinho/AI
  public XYSeries createPassiveLearningSerie(
      String metricName, List<AbstractEvaluation> evaluations) {

    XYSeries xyseriesPassive = new XYSeries("Passive learning");

    // Fill the series of passive learning
    int evalIndex = 0;
    for (AbstractEvaluation eval : evaluations) {

      if (evalIndex % reportFrecuency == 0) {
        xyseriesPassive.add(eval.getLabeledSetSize(), passiveEvaluation.getMetricValue(metricName));
      }
      ++evalIndex;
    }

    return xyseriesPassive;
  }
コード例 #3
0
  /** Evaluates the classifier using the test dataset. */
  @Override
  public void testModel() {

    try {
      // test phase with the actual model
      AbstractEvaluation evaluation = classifier.testModel(testData);

      evaluation.setLabeledSetSize(getLabelledData().getNumInstances());

      evaluation.setUnlabeledSetSize(getUnlabelledData().getNumInstances());

      evaluations.add(evaluation);

    } catch (Exception e) {

      Logger.getLogger(AbstractQueryStrategy.class.getName()).log(Level.SEVERE, null, e);
    }
  }