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;
}
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;
}
/** 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);
}
}