#include <sstream> #include <iostream> #include <fstream> #include <vector> #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 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 = metric->ComputeScore(J_i[a].eval_feats); float gb = metric->ComputeScore(J_i[b].eval_feats); 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, ds[sent_id]); J_i.WriteToFile(kbest_file); Sample(gamma, xi, J_i, 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; }