diff options
Diffstat (limited to 'rst_parser/mst_train.cc')
| -rw-r--r-- | rst_parser/mst_train.cc | 200 |
1 files changed, 199 insertions, 1 deletions
diff --git a/rst_parser/mst_train.cc b/rst_parser/mst_train.cc index 7b5af4c1..def23edb 100644 --- a/rst_parser/mst_train.cc +++ b/rst_parser/mst_train.cc @@ -1,12 +1,210 @@ #include "arc_factored.h" +#include <vector> #include <iostream> +#include <boost/program_options.hpp> +#include <boost/program_options/variables_map.hpp> + +#include "arc_ff.h" +#include "arc_ff_factory.h" +#include "stringlib.h" +#include "filelib.h" +#include "tdict.h" +#include "picojson.h" +#include "optimize.h" +#include "weights.h" using namespace std; +namespace po = boost::program_options; + +void InitCommandLine(int argc, char** argv, po::variables_map* conf) { + po::options_description opts("Configuration options"); + string cfg_file; + opts.add_options() + ("training_data,t",po::value<string>()->default_value("-"), "File containing training data (jsent format)") + ("feature_function,F",po::value<vector<string> >()->composing(), "feature function") + ("regularization_strength,C",po::value<double>()->default_value(1.0), "Regularization strength") + ("correction_buffers,m", po::value<int>()->default_value(10), "LBFGS correction buffers"); + po::options_description clo("Command line options"); + clo.add_options() + ("config,c", po::value<string>(&cfg_file), "Configuration file") + ("help,?", "Print this help message and exit"); + + po::options_description dconfig_options, dcmdline_options; + dconfig_options.add(opts); + dcmdline_options.add(dconfig_options).add(clo); + po::store(parse_command_line(argc, argv, dcmdline_options), *conf); + if (cfg_file.size() > 0) { + ReadFile rf(cfg_file); + po::store(po::parse_config_file(*rf.stream(), dconfig_options), *conf); + } + if (conf->count("help")) { + cerr << dcmdline_options << endl; + exit(1); + } +} + +struct TrainingInstance { + TaggedSentence ts; + EdgeSubset tree; + SparseVector<weight_t> features; +}; + +void ReadTraining(const string& fname, vector<TrainingInstance>* corpus, int rank = 0, int size = 1) { + ReadFile rf(fname); + istream& in = *rf.stream(); + string line; + string err; + int lc = 0; + bool flag = false; + while(getline(in, line)) { + ++lc; + if ((lc-1) % size != rank) continue; + if (rank == 0 && lc % 10 == 0) { cerr << '.' << flush; flag = true; } + if (rank == 0 && lc % 400 == 0) { cerr << " [" << lc << "]\n"; flag = false; } + size_t pos = line.rfind('\t'); + assert(pos != string::npos); + picojson::value obj; + picojson::parse(obj, line.begin() + pos, line.end(), &err); + if (err.size() > 0) { cerr << "JSON parse error in " << lc << ": " << err << endl; abort(); } + corpus->push_back(TrainingInstance()); + TrainingInstance& cur = corpus->back(); + TaggedSentence& ts = cur.ts; + EdgeSubset& tree = cur.tree; + assert(obj.is<picojson::object>()); + const picojson::object& d = obj.get<picojson::object>(); + const picojson::array& ta = d.find("tokens")->second.get<picojson::array>(); + for (unsigned i = 0; i < ta.size(); ++i) { + ts.words.push_back(TD::Convert(ta[i].get<picojson::array>()[0].get<string>())); + ts.pos.push_back(TD::Convert(ta[i].get<picojson::array>()[1].get<string>())); + } + const picojson::array& da = d.find("deps")->second.get<picojson::array>(); + for (unsigned i = 0; i < da.size(); ++i) { + const picojson::array& thm = da[i].get<picojson::array>(); + // get dep type here + short h = thm[2].get<double>(); + short m = thm[1].get<double>(); + if (h < 0) + tree.roots.push_back(m); + else + tree.h_m_pairs.push_back(make_pair(h,m)); + } + //cerr << TD::GetString(ts.words) << endl << TD::GetString(ts.pos) << endl << tree << endl; + } + if (flag) cerr << "\nRead " << lc << " training instances\n"; +} + +void AddFeatures(double prob, const SparseVector<double>& fmap, vector<double>* g) { + for (SparseVector<double>::const_iterator it = fmap.begin(); it != fmap.end(); ++it) + (*g)[it->first] += it->second * prob; +} + +double ApplyRegularizationTerms(const double C, + const vector<double>& weights, + vector<double>* g) { + assert(weights.size() == g->size()); + double reg = 0; + for (size_t i = 0; i < weights.size(); ++i) { +// const double prev_w_i = (i < prev_weights.size() ? prev_weights[i] : 0.0); + const double& w_i = weights[i]; + double& g_i = (*g)[i]; + reg += C * w_i * w_i; + g_i += 2 * C * w_i; + +// reg += T * (w_i - prev_w_i) * (w_i - prev_w_i); +// g_i += 2 * T * (w_i - prev_w_i); + } + return reg; +} int main(int argc, char** argv) { + int rank = 0; + int size = 1; + po::variables_map conf; + InitCommandLine(argc, argv, &conf); ArcFactoredForest af(5); - cerr << af(0,3) << endl; + ArcFFRegistry reg; + reg.Register("DistancePenalty", new ArcFFFactory<DistancePenalty>); + vector<TrainingInstance> corpus; + vector<boost::shared_ptr<ArcFeatureFunction> > ffs; + ffs.push_back(boost::shared_ptr<ArcFeatureFunction>(new DistancePenalty(""))); + ReadTraining(conf["training_data"].as<string>(), &corpus, rank, size); + vector<ArcFactoredForest> forests(corpus.size()); + SparseVector<double> empirical; + bool flag = false; + for (int i = 0; i < corpus.size(); ++i) { + TrainingInstance& cur = corpus[i]; + if (rank == 0 && (i+1) % 10 == 0) { cerr << '.' << flush; flag = true; } + if (rank == 0 && (i+1) % 400 == 0) { cerr << " [" << (i+1) << "]\n"; flag = false; } + for (int fi = 0; fi < ffs.size(); ++fi) { + ArcFeatureFunction& ff = *ffs[fi]; + ff.PrepareForInput(cur.ts); + SparseVector<weight_t> efmap; + for (int j = 0; j < cur.tree.h_m_pairs.size(); ++j) { + efmap.clear(); + ff.EgdeFeatures(cur.ts, cur.tree.h_m_pairs[j].first, + cur.tree.h_m_pairs[j].second, + &efmap); + cur.features += efmap; + } + for (int j = 0; j < cur.tree.roots.size(); ++j) { + efmap.clear(); + ff.EgdeFeatures(cur.ts, -1, cur.tree.roots[j], &efmap); + cur.features += efmap; + } + } + empirical += cur.features; + forests[i].resize(cur.ts.words.size()); + forests[i].ExtractFeatures(cur.ts, ffs); + } + if (flag) cerr << endl; + //cerr << "EMP: " << empirical << endl; //DE + vector<weight_t> weights(FD::NumFeats(), 0.0); + vector<weight_t> g(FD::NumFeats(), 0.0); + cerr << "features initialized\noptimizing...\n"; + boost::shared_ptr<BatchOptimizer> o; + o.reset(new LBFGSOptimizer(g.size(), conf["correction_buffers"].as<int>())); + int iterations = 1000; + for (int iter = 0; iter < iterations; ++iter) { + cerr << "ITERATION " << iter << " " << flush; + fill(g.begin(), g.end(), 0.0); + for (SparseVector<double>::const_iterator it = empirical.begin(); it != empirical.end(); ++it) + g[it->first] = -it->second; + double obj = -empirical.dot(weights); + // SparseVector<double> mfm; //DE + for (int i = 0; i < corpus.size(); ++i) { + forests[i].Reweight(weights); + double logz; + forests[i].EdgeMarginals(&logz); + //cerr << " O = " << (-corpus[i].features.dot(weights)) << " D=" << -logz << " OO= " << (-corpus[i].features.dot(weights) - logz) << endl; + obj -= logz; + int num_words = corpus[i].ts.words.size(); + for (int h = -1; h < num_words; ++h) { + for (int m = 0; m < num_words; ++m) { + if (h == m) continue; + const ArcFactoredForest::Edge& edge = forests[i](h,m); + const SparseVector<weight_t>& fmap = edge.features; + double prob = edge.edge_prob.as_float(); + if (prob < -0.000001) { cerr << "Prob < 0: " << prob << endl; prob = 0; } + if (prob > 1.000001) { cerr << "Prob > 1: " << prob << endl; prob = 1; } + AddFeatures(prob, fmap, &g); + //mfm += fmap * prob; // DE + } + } + } + //cerr << endl << "E: " << empirical << endl; // DE + //cerr << "M: " << mfm << endl; // DE + double r = ApplyRegularizationTerms(conf["regularization_strength"].as<double>(), weights, &g); + double gnorm = 0; + for (int i = 0; i < g.size(); ++i) + gnorm += g[i]*g[i]; + cerr << "OBJ=" << (obj+r) << "\t[F=" << obj << " R=" << r << "]\tGnorm=" << sqrt(gnorm) << endl; + obj += r; + assert(obj >= 0); + o->Optimize(obj, g, &weights); + Weights::ShowLargestFeatures(weights); + if (o->HasConverged()) { cerr << "CONVERGED\n"; break; } + } return 0; } |