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#include "arc_factored.h"
#include <vector>
#include <iostream>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
// #define HAVE_THREAD 1
#if HAVE_THREAD
#include <boost/thread.hpp>
#endif
#include "arc_ff.h"
#include "stringlib.h"
#include "filelib.h"
#include "tdict.h"
#include "dep_training.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)")
("weights,w",po::value<string>(), "Optional starting weights")
("output_every_i_iterations,I",po::value<unsigned>()->default_value(1), "Write weights every I iterations")
("regularization_strength,C",po::value<double>()->default_value(1.0), "Regularization strength")
#ifdef HAVE_CMPH
("cmph_perfect_feature_hash,h", po::value<string>(), "Load perfect hash function for features")
#endif
#if HAVE_THREAD
("threads,T",po::value<unsigned>()->default_value(1), "Number of threads")
#endif
("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);
}
}
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;
}
struct GradientWorker {
GradientWorker(int f,
int t,
vector<double>* w,
vector<TrainingInstance>* c,
vector<ArcFactoredForest>* fs) : obj(), weights(*w), from(f), to(t), corpus(*c), forests(*fs), g(w->size()) {}
void operator()() {
int every = (to - from) / 20;
if (!every) every++;
for (int i = from; i < to; ++i) {
if ((from == 0) && (i + 1) % every == 0) cerr << '.' << flush;
const int num_words = corpus[i].ts.words.size();
forests[i].Reweight(weights);
prob_t z;
forests[i].EdgeMarginals(&z);
obj -= log(z);
//cerr << " O = " << (-corpus[i].features.dot(weights)) << " D=" << -lz << " OO= " << (-corpus[i].features.dot(weights) - lz) << endl;
//cerr << " ZZ = " << zz << endl;
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
}
}
}
}
double obj;
vector<double>& weights;
const int from, to;
vector<TrainingInstance>& corpus;
vector<ArcFactoredForest>& forests;
vector<double> g; // local gradient
};
int main(int argc, char** argv) {
int rank = 0;
int size = 1;
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
if (conf.count("cmph_perfect_feature_hash")) {
cerr << "Loading perfect hash function from " << conf["cmph_perfect_feature_hash"].as<string>() << " ...\n";
FD::EnableHash(conf["cmph_perfect_feature_hash"].as<string>());
cerr << " " << FD::NumFeats() << " features in map\n";
}
ArcFeatureFunctions ffs;
vector<TrainingInstance> corpus;
TrainingInstance::ReadTrainingCorpus(conf["training_data"].as<string>(), &corpus, rank, size);
vector<weight_t> weights;
Weights::InitFromFile(conf["weights"].as<string>(), &weights);
vector<ArcFactoredForest> forests(corpus.size());
SparseVector<double> empirical;
cerr << "Extracting features...\n";
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; }
ffs.PrepareForInput(cur.ts);
SparseVector<weight_t> efmap;
for (int j = 0; j < cur.tree.h_m_pairs.size(); ++j) {
efmap.clear();
ffs.EdgeFeatures(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();
ffs.EdgeFeatures(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
weights.resize(FD::NumFeats(), 0.0);
vector<weight_t> g(FD::NumFeats(), 0.0);
cerr << "features initialized\noptimizing...\n";
boost::shared_ptr<BatchOptimizer> o;
#if HAVE_THREAD
unsigned threads = conf["threads"].as<unsigned>();
if (threads > corpus.size()) threads = corpus.size();
#else
const unsigned threads = 1;
#endif
int chunk = corpus.size() / threads;
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);
vector<boost::shared_ptr<GradientWorker> > jobs;
for (int from = 0; from < corpus.size(); from += chunk) {
int to = from + chunk;
if (to > corpus.size()) to = corpus.size();
jobs.push_back(boost::shared_ptr<GradientWorker>(new GradientWorker(from, to, &weights, &corpus, &forests)));
}
#if HAVE_THREAD
boost::thread_group tg;
for (int i = 0; i < threads; ++i)
tg.create_thread(boost::ref(*jobs[i]));
tg.join_all();
#else
(*jobs[0])();
#endif
for (int i = 0; i < threads; ++i) {
obj += jobs[i]->obj;
vector<double>& tg = jobs[i]->g;
for (unsigned j = 0; j < g.size(); ++j)
g[j] += tg[j];
}
// SparseVector<double> mfm; //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];
ostringstream ll;
ll << "ITER=" << (iter+1) << "\tOBJ=" << (obj+r) << "\t[F=" << obj << " R=" << r << "]\tGnorm=" << sqrt(gnorm);
cerr << ' ' << ll.str().substr(ll.str().find('\t')+1) << endl;
obj += r;
assert(obj >= 0);
o->Optimize(obj, g, &weights);
Weights::ShowLargestFeatures(weights);
const bool converged = o->HasConverged();
const char* ofname = converged ? "weights.final.gz" : "weights.cur.gz";
if (converged || ((iter+1) % conf["output_every_i_iterations"].as<unsigned>()) == 0) {
cerr << "writing..." << flush;
const string sl = ll.str();
Weights::WriteToFile(ofname, weights, true, &sl);
cerr << "done" << endl;
}
if (converged) { cerr << "CONVERGED\n"; break; }
}
return 0;
}
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