public int rank(List<List<String>> candidateFeatures) {
FeatureVector[] featureVectorArray =
TrainMalletMaxEntRank.candidateFeatures2FV(candidateFeatures, model.getAlphabet());
Instance instance = new Instance(featureVectorArray, null, null, null);
Labeling lab = model.classify(instance).getLabeling();
return Integer.parseInt(lab.getBestLabel().toString());
}
void showInterpolatedTCAccuracy(
InstanceList trainingInstanceList, InstanceList testingInstanceList) {
trainer = new MyClassifierTrainer(new RankMaxEntTrainer());
RankMaxEnt generalClassifier = (RankMaxEnt) trainer.train(trainingInstanceList);
InstanceList[] trainingInstanceLists = new InstanceList[3];
InstanceList[] testingInstanceLists = new InstanceList[3];
for (int i = 0; i < 3; i++) {
trainingInstanceLists[i] = new InstanceList(trainingInstanceList.getPipe());
testingInstanceLists[i] = new InstanceList(testingInstanceList.getPipe());
}
for (Instance instance : trainingInstanceList) {
Arg1RankInstance rankInstance = (Arg1RankInstance) instance;
Sentence sentence = rankInstance.document.getSentence(rankInstance.getArg2Line());
String conn = sentence.toString(rankInstance.connStart, rankInstance.connEnd).toLowerCase();
String category = connAnalyzer.getCategory(conn);
if (category == null) category = "Conj-adverbial";
if (category.startsWith("Coord")) {
trainingInstanceLists[0].add(instance);
} else if (category.startsWith("Sub")) {
trainingInstanceLists[1].add(instance);
} else {
trainingInstanceLists[2].add(instance);
}
}
for (Instance instance : testingInstanceList) {
Arg1RankInstance rankInstance = (Arg1RankInstance) instance;
Sentence sentence = rankInstance.document.getSentence(rankInstance.getArg2Line());
String conn = sentence.toString(rankInstance.connStart, rankInstance.connEnd).toLowerCase();
String category = connAnalyzer.getCategory(conn);
if (category == null) category = "Conj-adverbial";
if (category.startsWith("Coord")) {
testingInstanceLists[0].add(instance);
} else if (category.startsWith("Sub")) {
testingInstanceLists[1].add(instance);
} else {
testingInstanceLists[2].add(instance);
}
}
MyClassifierTrainer trainers[] = new MyClassifierTrainer[3];
RankMaxEnt classifiers[] = new RankMaxEnt[3];
double total = 0;
double correct = 0;
for (int i = 0; i < 3; i++) {
trainers[i] = new MyClassifierTrainer(new RankMaxEntTrainer());
classifiers[i] = (RankMaxEnt) trainers[i].train(trainingInstanceLists[i]);
total += testingInstanceLists[i].size();
// correct += getAccuracy(classifiers[i], testingInstanceLists[i]) *
// testingInstanceLists[i].size(); //accuracy * total
for (Instance instance : testingInstanceLists[i]) {
Arg1RankInstance rankInstance = (Arg1RankInstance) instance;
int trueIndex = rankInstance.trueArg1Candidate;
double genScores[] = new double[((FeatureVectorSequence) instance.getData()).size()];
generalClassifier.getClassificationScores(instance, genScores);
double tcScores[] = new double[((FeatureVectorSequence) instance.getData()).size()];
classifiers[i].getClassificationScores(instance, tcScores);
double max = 0;
int maxIndex = -1;
for (int j = 0; j < genScores.length; j++) {
double score = genScores[j] * 0.4 + tcScores[j] * 0.6;
if (score > max) {
max = score;
maxIndex = j;
}
}
if (maxIndex == trueIndex) {
correct++;
}
}
}
System.out.println("Using interpolated model:");
System.out.println("Total: " + total);
System.out.println("Correct: " + correct);
System.out.println("Accuracy: " + correct / total);
}