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#include <sstream>
#include <iostream>
#include <vector>
#include <limits>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "liblbfgs/lbfgs++.h"
#include "filelib.h"
#include "stringlib.h"
#include "weights.h"
#include "hg_io.h"
#include "kbest.h"
#include "viterbi.h"
#include "ns.h"
#include "ns_docscorer.h"
#include "candidate_set.h"
#include "risk.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");
opts.add_options()
("reference,r",po::value<vector<string> >(), "[REQD] Reference translation (tokenized text)")
("weights,w",po::value<string>(), "[REQD] Weights files from current iterations")
("input,i",po::value<string>()->default_value("-"), "Input file to map (- is STDIN)")
("evaluation_metric,m",po::value<string>()->default_value("IBM_BLEU"), "Evaluation metric (ibm_bleu, koehn_bleu, nist_bleu, ter, meteor, etc.)")
("kbest_repository,R",po::value<string>(), "Accumulate k-best lists from previous iterations (parameter is path to repository)")
("kbest_size,k",po::value<unsigned>()->default_value(500u), "Top k-hypotheses to extract")
("help,h", "Help");
po::options_description dcmdline_options;
dcmdline_options.add(opts);
po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
bool flag = false;
if (!conf->count("reference")) {
cerr << "Please specify one or more references using -r <REF.TXT>\n";
flag = true;
}
if (!conf->count("weights")) {
cerr << "Please specify weights using -w <WEIGHTS.TXT>\n";
flag = true;
}
if (flag || conf->count("help")) {
cerr << dcmdline_options << endl;
exit(1);
}
}
EvaluationMetric* metric = NULL;
struct RiskObjective {
explicit RiskObjective(const vector<training::CandidateSet>& tr) : training(tr) {}
double operator()(const vector<double>& x, double* g) const {
fill(g, g + x.size(), 0.0);
double obj = 0;
for (unsigned i = 0; i < training.size(); ++i) {
training::CandidateSetRisk risk(training[i], *metric);
SparseVector<double> tg;
double r = risk(x, &tg);
obj += r;
for (SparseVector<double>::iterator it = tg.begin(); it != tg.end(); ++it)
g[it->first] += it->second;
}
cerr << (1-(obj / training.size())) << endl;
return obj;
}
const vector<training::CandidateSet>& training;
};
double LearnParameters(const vector<training::CandidateSet>& training,
const double C1,
const unsigned memory_buffers,
vector<weight_t>* px) {
RiskObjective obj(training);
LBFGS<RiskObjective> lbfgs(px, obj, memory_buffers, C1);
lbfgs.MinimizeFunction();
return 0;
}
// runs lines 4--15 of rampion algorithm
int main(int argc, char** argv) {
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
const string evaluation_metric = conf["evaluation_metric"].as<string>();
metric = EvaluationMetric::Instance(evaluation_metric);
DocumentScorer ds(metric, conf["reference"].as<vector<string> >());
cerr << "Loaded " << ds.size() << " references for scoring with " << evaluation_metric << endl;
double goodsign = -1;
double badsign = -goodsign;
Hypergraph hg;
string last_file;
ReadFile in_read(conf["input"].as<string>());
string kbest_repo;
if (conf.count("kbest_repository")) {
kbest_repo = conf["kbest_repository"].as<string>();
MkDirP(kbest_repo);
}
istream &in=*in_read.stream();
const unsigned kbest_size = conf["kbest_size"].as<unsigned>();
vector<weight_t> weights;
const string weightsf = conf["weights"].as<string>();
Weights::InitFromFile(weightsf, &weights);
double t = 0;
for (unsigned i = 0; i < weights.size(); ++i)
t += weights[i] * weights[i];
if (t > 0) {
for (unsigned i = 0; i < weights.size(); ++i)
weights[i] /= sqrt(t);
}
string line, file;
vector<training::CandidateSet> kis;
cerr << "Loading hypergraphs...\n";
while(getline(in, line)) {
istringstream is(line);
int sent_id;
kis.resize(kis.size() + 1);
training::CandidateSet& curkbest = kis.back();
string kbest_file;
if (kbest_repo.size()) {
ostringstream os;
os << kbest_repo << "/kbest." << sent_id << ".txt.gz";
kbest_file = os.str();
if (FileExists(kbest_file))
curkbest.ReadFromFile(kbest_file);
}
is >> file >> sent_id;
ReadFile rf(file);
if (kis.size() % 5 == 0) { cerr << '.'; }
if (kis.size() % 200 == 0) { cerr << " [" << kis.size() << "]\n"; }
HypergraphIO::ReadFromJSON(rf.stream(), &hg);
hg.Reweight(weights);
curkbest.AddKBestCandidates(hg, kbest_size, ds[sent_id]);
if (kbest_file.size())
curkbest.WriteToFile(kbest_file);
}
cerr << "\nHypergraphs loaded.\n";
weights.resize(FD::NumFeats());
LearnParameters(kis, 0.0, 100, &weights);
Weights::WriteToFile("-", weights);
return 0;
}
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