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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()
("input,i",po::value<string>()->default_value("-"), "File containing test data (jsent format)")
("q_weights,q",po::value<string>(), "Arc-factored weights for proposal distribution (mandatory)")
("p_weights,p",po::value<string>(), "Weights for target distribution (optional)")
("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") || conf->count("q_weights") == 0) {
cerr << dcmdline_options << endl;
exit(1);
}
}
int main(int argc, char** argv) {
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
vector<weight_t> qweights, pweights;
Weights::InitFromFile(conf["q_weights"].as<string>(), &qweights);
if (conf.count("p_weights"))
Weights::InitFromFile(conf["p_weights"].as<string>(), &pweights);
const bool global = pweights.size() > 0;
ArcFeatureFunctions ffs;
GlobalFeatureFunctions gff;
ReadFile rf(conf["input"].as<string>());
istream* in = rf.stream();
TrainingInstance sent;
MT19937 rng;
int samples = conf["samples"].as<unsigned>();
int totroot = 0, root_right = 0, tot = 0, cor = 0;
while(TrainingInstance::ReadInstance(in, &sent)) {
ffs.PrepareForInput(sent.ts);
if (global) gff.PrepareForInput(sent.ts);
ArcFactoredForest forest(sent.ts.pos.size());
forest.ExtractFeatures(sent.ts, ffs);
forest.Reweight(qweights);
TreeSampler ts(forest);
double best_score = -numeric_limits<double>::infinity();
EdgeSubset best_tree;
for (int n = 0; n < samples; ++n) {
EdgeSubset tree;
ts.SampleRandomSpanningTree(&tree, &rng);
SparseVector<double> qfeats, gfeats;
tree.ExtractFeatures(sent.ts, ffs, &qfeats);
double score = 0;
if (global) {
gff.Features(sent.ts, tree, &gfeats);
score = (qfeats + gfeats).dot(pweights);
} else {
score = qfeats.dot(qweights);
}
if (score > best_score) {
best_tree = tree;
best_score = score;
}
}
cerr << "BEST SCORE: " << best_score << endl;
cout << best_tree << endl;
const bool sent_has_ref = sent.tree.h_m_pairs.size() > 0;
if (sent_has_ref) {
map<pair<short,short>, bool> ref;
for (int i = 0; i < sent.tree.h_m_pairs.size(); ++i)
ref[sent.tree.h_m_pairs[i]] = true;
int ref_root = sent.tree.roots.front();
if (ref_root == best_tree.roots.front()) { ++root_right; }
++totroot;
for (int i = 0; i < best_tree.h_m_pairs.size(); ++i) {
if (ref[best_tree.h_m_pairs[i]]) {
++cor;
}
++tot;
}
}
}
cerr << "F = " << (double(cor + root_right) / (tot + totroot)) << endl;
return 0;
}
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