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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 "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"
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")
("cccp_iterations,I", po::value<unsigned>()->default_value(10u), "CCCP iterations (T')")
("ssd_iterations,J", po::value<unsigned>()->default_value(5u), "Stochastic subgradient iterations (T'')")
("eta", po::value<double>()->default_value(1e-4), "Step size")
("regularization_strength,C", po::value<double>()->default_value(1.0), "L2 regularization strength")
("alpha,a", po::value<double>()->default_value(10.0), "Cost scale (alpha); alpha * [1-metric(y,y')]")
("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);
}
}
struct GainFunction {
explicit GainFunction(const EvaluationMetric* m) : metric(m) {}
float operator()(const SufficientStats& eval_feats) const {
float g = metric->ComputeScore(eval_feats);
if (!metric->IsErrorMetric()) g = 1 - g;
return g;
}
const EvaluationMetric* metric;
};
template <typename GainFunc>
void CostAugmentedSearch(const GainFunc& gain,
const training::CandidateSet& cs,
const SparseVector<double>& w,
double alpha,
SparseVector<double>* fmap) {
unsigned best_i = 0;
double best = -numeric_limits<double>::infinity();
for (unsigned i = 0; i < cs.size(); ++i) {
double s = cs[i].fmap.dot(w) + alpha * gain(cs[i].eval_feats);
if (s > best) {
best = s;
best_i = i;
}
}
*fmap = cs[best_i].fmap;
}
// 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>();
EvaluationMetric* 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>();
const unsigned tp = conf["cccp_iterations"].as<unsigned>();
const unsigned tpp = conf["ssd_iterations"].as<unsigned>();
const double eta = conf["eta"].as<double>();
const double reg = conf["regularization_strength"].as<double>();
const double alpha = conf["alpha"].as<double>();
SparseVector<weight_t> weights;
{
vector<weight_t> vweights;
const string weightsf = conf["weights"].as<string>();
Weights::InitFromFile(weightsf, &vweights);
Weights::InitSparseVector(vweights, &weights);
}
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::ReadFromBinary(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";
vector<SparseVector<weight_t> > goals(kis.size()); // f(x_i,y+,h+)
SparseVector<weight_t> fear; // f(x,y-,h-)
const GainFunction gain(metric);
for (unsigned iterp = 1; iterp <= tp; ++iterp) {
cerr << "CCCP Iteration " << iterp << endl;
for (unsigned i = 0; i < goals.size(); ++i)
CostAugmentedSearch(gain, kis[i], weights, goodsign * alpha, &goals[i]);
for (unsigned iterpp = 1; iterpp <= tpp; ++iterpp) {
cerr << " SSD Iteration " << iterpp << endl;
for (unsigned i = 0; i < goals.size(); ++i) {
CostAugmentedSearch(gain, kis[i], weights, badsign * alpha, &fear);
weights -= weights * (eta * reg / goals.size());
weights += (goals[i] - fear) * eta;
}
}
}
vector<weight_t> w;
weights.init_vector(&w);
Weights::WriteToFile("-", w);
return 0;
}
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