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#include <sstream>
#include <iostream>
#include <fstream>
#include <vector>
#include <tr1/unordered_map>
#include <boost/functional/hash.hpp>
#include <boost/shared_ptr.hpp>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "candidate_set.h"
#include "sampler.h"
#include "filelib.h"
#include "stringlib.h"
#include "weights.h"
#include "inside_outside.h"
#include "hg_io.h"
#include "ns.h"
#include "ns_docscorer.h"
// This is Figure 4 (Algorithm Sampler) from Hopkins&May (2011)
using namespace std;
namespace po = boost::program_options;
boost::shared_ptr<MT19937> rng;
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")
("kbest_repository,K",po::value<string>()->default_value("./kbest"),"K-best list repository (directory)")
("input,i",po::value<string>()->default_value("-"), "Input file to map (- is STDIN)")
("source,s",po::value<string>()->default_value(""), "Source file (ignored, except for AER)")
("evaluation_metric,m",po::value<string>()->default_value("IBM_BLEU"), "Evaluation metric (ibm_bleu, koehn_bleu, nist_bleu, ter, meteor, etc.)")
("kbest_size,k",po::value<unsigned>()->default_value(1500u), "Top k-hypotheses to extract")
("candidate_pairs,G", po::value<unsigned>()->default_value(5000u), "Number of pairs to sample per hypothesis (Gamma)")
("best_pairs,X", po::value<unsigned>()->default_value(50u), "Number of pairs, ranked by magnitude of objective delta, to retain (Xi)")
("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
("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 ThresholdAlpha {
explicit ThresholdAlpha(double t = 0.05) : threshold(t) {}
double operator()(double mag) const {
if (mag < threshold) return 0.0; else return 1.0;
}
const double threshold;
};
struct TrainingInstance {
TrainingInstance(const SparseVector<weight_t>& feats, bool positive, float diff) : x(feats), y(positive), gdiff(diff) {}
SparseVector<weight_t> x;
#undef DEBUGGING_PRO
#ifdef DEBUGGING_PRO
vector<WordID> a;
vector<WordID> b;
#endif
bool y;
float gdiff;
};
#ifdef DEBUGGING_PRO
ostream& operator<<(ostream& os, const TrainingInstance& d) {
return os << d.gdiff << " y=" << d.y << "\tA:" << TD::GetString(d.a) << "\n\tB: " << TD::GetString(d.b) << "\n\tX: " << d.x;
}
#endif
struct DiffOrder {
bool operator()(const TrainingInstance& a, const TrainingInstance& b) const {
return a.gdiff > b.gdiff;
}
};
void Sample(const unsigned gamma,
const unsigned xi,
const training::CandidateSet& J_i,
const SegmentEvaluator& scorer,
const EvaluationMetric* metric,
vector<TrainingInstance>* pv) {
const bool invert_score = metric->IsErrorMetric();
vector<TrainingInstance> v1, v2;
float avg_diff = 0;
for (unsigned i = 0; i < gamma; ++i) {
const size_t a = rng->inclusive(0, J_i.size() - 1)();
const size_t b = rng->inclusive(0, J_i.size() - 1)();
if (a == b) continue;
float ga = J_i[a].g(scorer, metric);
float gb = J_i[b].g(scorer, metric);
bool positive = gb < ga;
if (invert_score) positive = !positive;
const float gdiff = fabs(ga - gb);
if (!gdiff) continue;
avg_diff += gdiff;
SparseVector<weight_t> xdiff = (J_i[a].fmap - J_i[b].fmap).erase_zeros();
if (xdiff.empty()) {
cerr << "Empty diff:\n " << TD::GetString(J_i[a].ewords) << endl << "x=" << J_i[a].fmap << endl;
cerr << " " << TD::GetString(J_i[b].ewords) << endl << "x=" << J_i[b].fmap << endl;
continue;
}
v1.push_back(TrainingInstance(xdiff, positive, gdiff));
#ifdef DEBUGGING_PRO
v1.back().a = J_i[a].hyp;
v1.back().b = J_i[b].hyp;
cerr << "N: " << v1.back() << endl;
#endif
}
avg_diff /= v1.size();
for (unsigned i = 0; i < v1.size(); ++i) {
double p = 1.0 / (1.0 + exp(-avg_diff - v1[i].gdiff));
// cerr << "avg_diff=" << avg_diff << " gdiff=" << v1[i].gdiff << " p=" << p << endl;
if (rng->next() < p) v2.push_back(v1[i]);
}
vector<TrainingInstance>::iterator mid = v2.begin() + xi;
if (xi > v2.size()) mid = v2.end();
partial_sort(v2.begin(), mid, v2.end(), DiffOrder());
copy(v2.begin(), mid, back_inserter(*pv));
#ifdef DEBUGGING_PRO
if (v2.size() >= 5) {
for (int i =0; i < (mid - v2.begin()); ++i) {
cerr << v2[i] << endl;
}
cerr << pv->back() << endl;
}
#endif
}
int main(int argc, char** argv) {
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
if (conf.count("random_seed"))
rng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
else
rng.reset(new MT19937);
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;
Hypergraph hg;
string last_file;
ReadFile in_read(conf["input"].as<string>());
istream &in=*in_read.stream();
const unsigned kbest_size = conf["kbest_size"].as<unsigned>();
const unsigned gamma = conf["candidate_pairs"].as<unsigned>();
const unsigned xi = conf["best_pairs"].as<unsigned>();
string weightsf = conf["weights"].as<string>();
vector<weight_t> weights;
Weights::InitFromFile(weightsf, &weights);
string kbest_repo = conf["kbest_repository"].as<string>();
MkDirP(kbest_repo);
while(in) {
vector<TrainingInstance> v;
string line;
getline(in, line);
if (line.empty()) continue;
istringstream is(line);
int sent_id;
string file;
// path-to-file (JSON) sent_id
is >> file >> sent_id;
ReadFile rf(file);
ostringstream os;
training::CandidateSet J_i;
os << kbest_repo << "/kbest." << sent_id << ".txt.gz";
const string kbest_file = os.str();
if (FileExists(kbest_file))
J_i.ReadFromFile(kbest_file);
HypergraphIO::ReadFromJSON(rf.stream(), &hg);
hg.Reweight(weights);
J_i.AddKBestCandidates(hg, kbest_size);
J_i.WriteToFile(kbest_file);
Sample(gamma, xi, J_i, *ds[sent_id], metric, &v);
for (unsigned i = 0; i < v.size(); ++i) {
const TrainingInstance& vi = v[i];
cout << vi.y << "\t" << vi.x << endl;
cout << (!vi.y) << "\t" << (vi.x * -1.0) << endl;
}
}
return 0;
}
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