public DocumentStream(long ts, String rootDir, FeedSettings settings) {
this.ts = ts;
this.rootDir = rootDir + "/" + ts + "/";
// this.maxWordsPerTopic =maxWordsPerTopic;
this.settings = settings;
list = LdaModel.createInstanceList(this.ts);
testing = new InstanceList(list.getPipe());
lda = new LdaModel();
}
public SVM train(InstanceList trainingList) {
svm_problem problem = new svm_problem();
problem.l = trainingList.size();
problem.x = new svm_node[problem.l][];
problem.y = new double[problem.l];
for (int i = 0; i < trainingList.size(); i++) {
Instance instance = trainingList.get(i);
svm_node[] input = SVM.getSvmNodes(instance);
if (input == null) {
continue;
}
int labelIndex = ((Label) instance.getTarget()).getIndex();
problem.x[i] = input;
problem.y[i] = labelIndex;
}
int max_index = trainingList.getDataAlphabet().size();
if (param.gamma == 0 && max_index > 0) {
param.gamma = 1.0 / max_index;
}
// int numLabels = trainingList.getTargetAlphabet().size();
// int[] weight_label = new int[numLabels];
// double[] weight = trainingList.targetLabelDistribution().getValues();
// double minValue = Double.MAX_VALUE;
//
// for (int i = 0; i < weight.length; i++) {
// if (minValue > weight[i]) {
// minValue = weight[i];
// }
// }
//
// for (int i = 0; i < weight.length; i++) {
// weight_label[i] = i;
// weight[i] = weight[i] / minValue;
// }
//
// param.weight_label = weight_label;
// param.weight = weight;
String error_msg = svm.svm_check_parameter(problem, param);
if (error_msg != null) {
System.err.print("Error: " + error_msg + "\n");
System.exit(1);
}
svm_model model = svm.svm_train(problem, param);
classifier = new SVM(model, trainingList.getPipe());
return classifier;
}
/**
* Create and train a CRF model from the given training data, optionally testing it on the given
* test data.
*
* @param training training data
* @param testing test data (possibly <code>null</code>)
* @param eval accuracy evaluator (possibly <code>null</code>)
* @param orders label Markov orders (main and backoff)
* @param defaultLabel default label
* @param forbidden regular expression specifying impossible label transitions <em>current</em>
* <code>,</code><em>next</em> (<code>null</code> indicates no forbidden transitions)
* @param allowed regular expression specifying allowed label transitions (<code>null</code>
* indicates everything is allowed that is not forbidden)
* @param connected whether to include even transitions not occurring in the training data.
* @param iterations number of training iterations
* @param var Gaussian prior variance
* @return the trained model
*/
public static CRF train(
InstanceList training,
InstanceList testing,
TransducerEvaluator eval,
int[] orders,
String defaultLabel,
String forbidden,
String allowed,
boolean connected,
int iterations,
double var,
CRF crf) {
Pattern forbiddenPat = Pattern.compile(forbidden);
Pattern allowedPat = Pattern.compile(allowed);
if (crf == null) {
crf = new CRF(training.getPipe(), (Pipe) null);
String startName =
crf.addOrderNStates(
training, orders, null, defaultLabel, forbiddenPat, allowedPat, connected);
CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
crft.setGaussianPriorVariance(var);
for (int i = 0; i < crf.numStates(); i++)
crf.getState(i).setInitialWeight(Transducer.IMPOSSIBLE_WEIGHT);
crf.getState(startName).setInitialWeight(0.0);
}
logger.info("Training on " + training.size() + " instances");
if (testing != null) logger.info("Testing on " + testing.size() + " instances");
CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
if (featureInductionOption.value) {
crft.trainWithFeatureInduction(
training, null, testing, eval, iterations, 10, 20, 500, 0.5, false, null);
} else {
boolean converged;
for (int i = 1; i <= iterations; i++) {
converged = crft.train(training, 1);
if (i % 1 == 0 && eval != null) // Change the 1 to higher integer to evaluate less often
eval.evaluate(crft);
if (viterbiOutputOption.value && i % 10 == 0)
new ViterbiWriter(
"", new InstanceList[] {training, testing}, new String[] {"training", "testing"})
.evaluate(crft);
if (converged) break;
}
}
return crf;
}
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);
}
double[] getTypeSpecificAccuracy(
InstanceList trainingInstanceList, InstanceList testingInstanceList, boolean show) {
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];
Classifier classifiers[] = new Classifier[3];
double total = 0;
double correct = 0;
for (int i = 0; i < 3; i++) {
trainers[i] = new MyClassifierTrainer(new RankMaxEntTrainer());
classifiers[i] = trainers[i].train(trainingInstanceLists[i]);
total += testingInstanceLists[i].size();
correct +=
getAccuracy(classifiers[i], testingInstanceLists[i])
* testingInstanceLists[i].size(); // accuracy * total
}
if (show) {
System.out.println("Using type specific models:");
System.out.println("Total: " + total);
System.out.println("Correct: " + correct);
System.out.println("Accuracy: " + correct / total);
}
return new double[] {total, correct, 1.0 * correct / total};
}
public InstanceList getInstances() {
InstanceList ret = new InstanceList(m_ilist.getPipe());
for (int ii = 0; ii < m_instIndices.length; ii++) ret.add(m_ilist.get(m_instIndices[ii]));
return ret;
}
public void cleanup() {
list = new InstanceList(list.getPipe());
}
