public void train(String[] trainSections) throws IOException {
pipe = defaultPipe();
InstanceList trainingInstanceList = prepareInstanceList(trainSections);
/*NFoldEvaluator evaluator = new NFoldEvaluator();
evaluator.evaluate(trainer, trainingInstanceList, 10); */
/*InstanceList[] instanceLists =
trainingInstanceList.splitInOrder(new double[]{0.9, 0.1});
Classifier classifier = trainer.train(instanceLists[0]);
showAccuracy(classifier, instanceLists[1]);*/
// showNFoldAccuracy(trainingInstanceList, 10, 2633);
trainer.train(trainingInstanceList);
// showNFoldTypeSpecificAccuracy(trainingInstanceList, 10);
}
public void train(String[] trainSections, String[] testSections) throws IOException {
pipe = defaultPipe();
InstanceList trainingInstanceList = prepareInstanceList(trainSections);
InstanceList testingInstanceList = prepareInstanceList(testSections);
// Classifier classifier = trainer.train(trainingInstanceList, testingInstanceList);
Classifier classifier = trainer.train(trainingInstanceList);
System.out.println("training size: " + trainingInstanceList.size());
System.out.println("testing size: " + testingInstanceList.size());
// showAccuracy(classifier, testingInstanceList);
// getTypeSpecificAccuracy(trainingInstanceList, testingInstanceList, true);
// showInterpolatedTCAccuracy(trainingInstanceList, testingInstanceList);
}
private void showNFoldAccuracy(InstanceList instanceList, int n, int count) {
InstanceList.CrossValidationIterator cvIt = instanceList.crossValidationIterator(n);
double accuracies[] = new double[n];
double accuracy = 0;
int run = 0;
double totalTP = 0;
while (cvIt.hasNext()) {
InstanceList[] nextSplit = cvIt.nextSplit();
InstanceList trainingInstances = nextSplit[0];
InstanceList testingInstances = nextSplit[1];
trainer = new MyClassifierTrainer(new RankMaxEntTrainer());
Classifier classifier = trainer.train(trainingInstances);
accuracies[run] = getAccuracy(classifier, testingInstances);
accuracy += accuracies[run];
totalTP += accuracies[run] * testingInstances.size();
run++;
}
System.out.println(n + "-Fold accuracy(avg): " + accuracy / n);
System.out.println("Total tp:" + totalTP);
System.out.println("Total count:" + count);
System.out.println(n + "-Fold accuracy: " + totalTP / count);
}
public void save(String fileName) {
trainer.saveModel(fileName);
}
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);
}
