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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 "timing_stats.h"
#include "arc_ff.h"
#include "dep_training.h"
#include "stringlib.h"
#include "filelib.h"
#include "tdict.h"
#include "weights.h"
#include "rst.h"
#include "global_ff.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)")
("q_weights,q",po::value<string>(), "Arc-factored weights for proposal distribution")
("samples,n",po::value<unsigned>()->default_value(1000), "Number of samples");
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);
}
}
int main(int argc, char** argv) {
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
vector<weight_t> qweights(FD::NumFeats(), 0.0);
Weights::InitFromFile(conf["q_weights"].as<string>(), &qweights);
vector<TrainingInstance> corpus;
ArcFeatureFunctions ffs;
GlobalFeatureFunctions gff;
TrainingInstance::ReadTrainingCorpus(conf["training_data"].as<string>(), &corpus);
vector<ArcFactoredForest> forests(corpus.size());
vector<prob_t> zs(corpus.size());
SparseVector<double> empirical;
bool flag = false;
for (int i = 0; i < corpus.size(); ++i) {
TrainingInstance& cur = corpus[i];
if ((i+1) % 10 == 0) { cerr << '.' << flush; flag = true; }
if ((i+1) % 400 == 0) { cerr << " [" << (i+1) << "]\n"; flag = false; }
SparseVector<weight_t> efmap;
ffs.PrepareForInput(cur.ts);
gff.PrepareForInput(cur.ts);
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;
}
efmap.clear();
gff.Features(cur.ts, cur.tree, &efmap);
cur.features += efmap;
empirical += cur.features;
forests[i].resize(cur.ts.words.size());
forests[i].ExtractFeatures(cur.ts, ffs);
forests[i].Reweight(qweights);
forests[i].EdgeMarginals(&zs[i]);
zs[i] = prob_t::One() / zs[i];
// cerr << zs[i] << endl;
forests[i].Reweight(qweights); // EdgeMarginals overwrites edge_prob
}
if (flag) cerr << endl;
MT19937 rng;
SparseVector<double> model_exp;
SparseVector<double> weights;
Weights::InitSparseVector(qweights, &weights);
int samples = conf["samples"].as<unsigned>();
for (int i = 0; i < corpus.size(); ++i) {
#if 0
forests[i].EdgeMarginals();
model_exp.clear();
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();
model_exp += fmap * prob;
}
}
cerr << "TRUE EXP: " << model_exp << endl;
forests[i].Reweight(weights);
#endif
TreeSampler ts(forests[i]);
prob_t zhat = prob_t::Zero();
SparseVector<prob_t> sampled_exp;
for (int n = 0; n < samples; ++n) {
EdgeSubset tree;
ts.SampleRandomSpanningTree(&tree, &rng);
SparseVector<double> qfeats, gfeats;
tree.ExtractFeatures(corpus[i].ts, ffs, &qfeats);
prob_t u; u.logeq(qfeats.dot(qweights));
const prob_t q = u / zs[i]; // proposal mass
gff.Features(corpus[i].ts, tree, &gfeats);
SparseVector<double> tot_feats = qfeats + gfeats;
u.logeq(tot_feats.dot(weights));
prob_t w = u / q;
zhat += w;
for (SparseVector<double>::const_iterator it = tot_feats.begin(); it != tot_feats.end(); ++it)
sampled_exp.add_value(it->first, w * prob_t(it->second));
}
sampled_exp /= zhat;
SparseVector<double> tot_m;
for (SparseVector<prob_t>::const_iterator it = sampled_exp.begin(); it != sampled_exp.end(); ++it)
tot_m.add_value(it->first, it->second.as_float());
//cerr << "DIFF: " << (tot_m - corpus[i].features) << endl;
const double eta = 0.03;
weights -= (tot_m - corpus[i].features) * eta;
}
cerr << "WEIGHTS.\n";
cerr << weights << endl;
return 0;
}
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