void develop(
List<Instance> trn, List<Instance> tst, OnlineOptimizer op, int miniBatch, boolean sparse) {
DoubleIntPair best = new DoubleIntPair(0d, 0);
int yhat, count;
float[] scores;
String label;
double acc;
for (int epoch = 0; epoch < 50; epoch++) {
// training
count = 0;
for (Instance instance : trn) {
op.train(instance);
if (++count == miniBatch) {
op.updateMiniBatch();
count = 0;
}
}
if (count > 0) op.updateMiniBatch();
// evaluating
count = 0;
for (Instance instance : tst) {
scores = op.scores(instance.getFeatureVector());
yhat = MLUtils.argmax(scores);
label = op.getLabel(yhat);
if (instance.isStringLabel(label)) count++;
}
acc = MathUtils.accuracy(count, tst.size());
System.out.printf("%4d: %5.2f\n", epoch, acc);
if (best.d < acc) best.set(acc, epoch);
}
System.out.printf("best: %5.2f at %d epoch\n", best.d, best.i);
}
public void run(FeatureTemplate<POSNode, POSState<POSNode>> template, int type, boolean average)
throws Exception {
final String root = "/Users/jdchoi/Documents/Data/experiments/wsj/wsj-pos/";
TSVReader<POSNode> reader = new TSVReader<>(new POSIndex(0, 1));
List<String> trnFiles = FileUtils.getFileList(root + "trn/", "pos");
List<String> devFiles = FileUtils.getFileList(root + "dev/", "pos");
Collections.sort(trnFiles);
Collections.sort(devFiles);
// collect ambiguity classes
AmbiguityClassMap map = new AmbiguityClassMap();
iterate(reader, trnFiles, nodes -> map.add(nodes));
map.expand(0.4);
// collect training instances
StringModel model = new StringModel(new MultinomialWeightVector(), false);
POSTagger<POSNode> tagger = new POSTagger<>(model);
tagger.setFlag(NLPFlag.TRAIN);
tagger.setAmbiguityClassMap(map);
tagger.setFeatureTemplate(template);
iterate(reader, trnFiles, nodes -> tagger.process(nodes));
// map string into a vector space
final int label_cutoff = 0;
final int feature_cutoff = 0;
model.vectorize(label_cutoff, feature_cutoff, false);
// train the statistical model
final double learning_rate = 0.02;
final int epochs = 1;
WeightVector weight = model.getWeightVector();
OnlineOptimizer sgd = null;
switch (type) {
case 0:
sgd = new Perceptron(weight, average, learning_rate);
break;
case 1:
sgd = new AdaGrad(weight, average, learning_rate);
break;
case 2:
sgd = new LogisticRegression(weight, average, learning_rate);
break;
}
Eval eval = new AccuracyEval();
tagger.setFlag(NLPFlag.EVALUATE);
tagger.setEval(eval);
DoubleIntPair best = new DoubleIntPair(-1, -1);
double currScore;
for (int i = 0; i < epochs; i++) {
sgd.train(model.getInstanceList());
eval.clear();
iterate(reader, devFiles, nodes -> tagger.process(nodes));
currScore = eval.score();
System.out.printf("%4d: %5.2f\n", i, currScore);
if (best.d < currScore) best.set(currScore, i);
}
System.out.printf("Best: %d - %5.2f\n", best.i, best.d);
// System.out.println("Saving");
// ObjectOutputStream out = IOUtils.createObjectXZBufferedOutputStream("tmp");
// out.writeObject(tagger);
// out.close();
//
// System.out.println("Loading");
// ObjectInputStream in = IOUtils.createObjectXZBufferedInputStream("tmp");
// NLPComponent<POSNode,POSState<POSNode>> component =
// (NLPComponent<POSNode,POSState<POSNode>>)in.readObject();
// eval.clear();
// component.setEval(eval);
// component.setFlag(NLPFlag.EVALUATE);
//
// System.out.println("Decoding");
// iterate(reader, devFiles, nodes -> component.process(nodes));
// System.out.println(eval.score());
}
