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#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);
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;
}
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