public void printPerformance(InstanceList instanceList, String description) {
System.out.println(description);
System.out.println("Accuracy: " + classifier.getAccuracy(instanceList));
LabelAlphabet labelAlphabet = classifier.getLabelAlphabet();
Iterator iterator = labelAlphabet.iterator();
while (iterator.hasNext()) {
Object label = iterator.next();
double p = classifier.getPrecision(instanceList, label);
double r = classifier.getRecall(instanceList, label);
double f1 = classifier.getF1(instanceList, label);
System.out.println("Precision[" + label + "] = " + p);
System.out.println("Recall[" + label + "] = " + r);
System.out.println("F1[" + label + "] = " + f1);
System.out.println("");
}
ExtendedTrial trial = new ExtendedTrial(classifier, instanceList);
System.out.println("Overall performance\n=====");
System.out.println("Precision: " + trial.getPrecision());
System.out.println("Recall: " + trial.getRecall());
System.out.println("F1: " + trial.getF1());
System.out.println("Fall-out: " + trial.getFallOut());
trial.showErrors();
}
private Label labelForTok(String tok, int lvl) {
while (labelDicts.size() <= lvl) {
labelDicts.add(new LabelAlphabet());
}
LabelAlphabet dict = (LabelAlphabet) labelDicts.get(lvl);
return dict.lookupLabel(tok);
}
@Override
public Instance pipe(Instance carrier) {
Arg1RankInstance instance = (Arg1RankInstance) carrier;
Document document = (Document) instance.getData();
List<Pair<Integer, Integer>> candidates = instance.getCandidates();
int connStart = instance.getConnStart();
int connEnd = instance.getConnEnd();
int arg2Line = instance.getArg2Line();
int arg2HeadPos = instance.getArg2HeadPos();
FeatureVector fvs[] = new FeatureVector[candidates.size()];
for (int i = 0; i < candidates.size(); i++) {
Pair<Integer, Integer> candidate = candidates.get(i);
PropertyList pl = null;
pl = addBaselineFeatures(pl, document, candidate, arg2Line, arg2HeadPos, connStart, connEnd);
pl =
addConstituentFeatures(
pl, document, candidate, arg2Line, arg2HeadPos, connStart, connEnd);
pl =
addDependencyFeatures(pl, document, candidate, arg2Line, arg2HeadPos, connStart, connEnd);
// pl = addLexicoSyntacticFeatures(pl, document, candidate, arg2Line, arg2HeadPos, connStart,
// connEnd);
fvs[i] = new FeatureVector(getDataAlphabet(), pl, true, true);
}
// set target label
LabelAlphabet ldict = (LabelAlphabet) getTargetAlphabet();
carrier.setTarget(ldict.lookupLabel(String.valueOf(instance.getTrueArg1Candidate())));
carrier.setData(new FeatureVectorSequence(fvs));
return carrier;
}
/**
* converts the sentence based instance list into a token based one This is needed for the
* ME-version of JET (JetMeClassifier)
*
* @param METrainerDummyPipe
* @param inst just the features for one sentence to be transformed
* @return
*/
public static InstanceList convertFeatsforClassifier(
final Pipe METrainerDummyPipe, final Instance inst) {
final InstanceList iList = new InstanceList(METrainerDummyPipe);
final FeatureVectorSequence fvs = (FeatureVectorSequence) inst.getData();
final LabelSequence ls = (LabelSequence) inst.getTarget();
final LabelAlphabet ldict = (LabelAlphabet) ls.getAlphabet();
final Object source = inst.getSource();
final Object name = inst.getName();
if (ls.size() != fvs.size()) {
System.err.println(
"failed making token instances: size of labelsequence != size of featue vector sequence: "
+ ls.size()
+ " - "
+ fvs.size());
System.exit(-1);
}
for (int j = 0; j < fvs.size(); j++) {
final Instance I =
new Instance(fvs.getFeatureVector(j), ldict.lookupLabel(ls.get(j)), name, source);
iList.add(I);
}
return iList;
}