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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 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;
+}
+
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