void CoNLLWriter::write(const Instance& inst) {
size_t len = inst.size();
bool predicted = (inst.predict_heads.size() == len &&
inst.predict_deprels.size() == len);
for (size_t i = 1; i < len; ++ i) {
f << i << "\t" // 0 - index
<< inst.raw_forms[i] << "\t" // 1 - form
<< inst.lemmas[i] << "\t" // 2 - lemma
<< inst.postags[i] << "\t" // 3 - postag
<< "_\t" // 4 - unknown
<< "_\t" // 5 - unknown
<< inst.heads[i] << "\t" // 6 - heads
<< inst.deprels[i] << "\t" // 7 - deprels
<< (predicted ? to_str(inst.predict_heads[i]) : "_")
<< "\t"
<< (predicted ? inst.predict_deprels[i] : "_")
<< std::endl;
}
f << std::endl;
}
void PostaggerFrontend::train(void) {
// read in training instance
INFO_LOG("trace: reading reference dataset ...");
if (!read_instances(train_opt.train_file.c_str())) {
ERROR_LOG("Training file doesn't exist.");
}
INFO_LOG("trace: %d sentences is loaded.", train_dat.size());
model = new Model(Extractor::num_templates());
// build tag dictionary, map string tag to index
INFO_LOG("report: start building configuration ...");
build_configuration();
INFO_LOG("report: build configuration is done.");
INFO_LOG("report: number of postags: %d", model->labels.size());
// build feature space from the training instance
INFO_LOG("report: start building feature space ...");
build_feature_space();
INFO_LOG("report: building feature space is done.");
INFO_LOG("report: number of features: %d", model->space.num_features());
model->param.realloc(model->space.dim());
INFO_LOG("report: allocate %d dimensition parameter.", model->space.dim());
int nr_groups = model->space.num_groups();
std::vector<int> groupwise_update_counters;
if (train_opt.rare_feature_threshold > 0) {
groupwise_update_counters.resize(nr_groups, 0);
INFO_LOG("report: allocate %d update-time counters", nr_groups);
} else {
INFO_LOG("report: model truncation is inactived.");
}
int best_iteration = -1;
double best_p = -1.;
std::vector<size_t> update_counts;
for (int iter = 0; iter < train_opt.max_iter; ++ iter) {
INFO_LOG("Training iteraition #%d", (iter + 1));
size_t interval= train_dat.size() / 10;
for (size_t i = 0; i < train_dat.size(); ++ i) {
Instance* inst = train_dat[i];
extract_features((*inst), &ctx, false);
calculate_scores((*inst), ctx, false, &scm);
decoder.decode(scm, inst->predict_tagsidx);
collect_features(model, ctx.uni_features, inst->tagsidx, ctx.correct_features);
collect_features(model, ctx.uni_features, inst->predict_tagsidx, ctx.predict_features);
SparseVec updated_features;
updated_features.add(ctx.correct_features, 1.);
updated_features.add(ctx.predict_features, -1.);
learn(train_opt.algorithm, updated_features,
iter*train_dat.size() + 1, inst->num_errors(), model);
if (train_opt.rare_feature_threshold > 0) {
increase_groupwise_update_counts(model, updated_features, update_counts);
}
ctx.clear();
if ((i+1) % interval == 0) {
INFO_LOG("training: %d0%% (%d) instances is trained.", ((i+1)/interval), i+1);
}
}
INFO_LOG("trace: %d instances is trained.", train_dat.size());
model->param.flush( train_dat.size() * (iter + 1) );
Model* new_model = new Model(Extractor::num_templates());
erase_rare_features(model, new_model, train_opt.rare_feature_threshold,
update_counts);
std::swap(model, new_model);
double p;
evaluate(p);
if(p > best_p){
best_p = p;
best_iteration = iter;
}
std::string saved_model_file = (train_opt.model_name+ "."+ to_str(iter));
std::ofstream ofs(saved_model_file.c_str(), std::ofstream::binary);
std::swap(model, new_model);
new_model->save(model_header, Parameters::kDumpAveraged, ofs);
delete new_model;
INFO_LOG("trace: model for iteration #%d is saved to %s", iter+1, saved_model_file.c_str());
}
INFO_LOG("Best result (iteration = %d) : P = %lf", best_iteration, best_p);
}
