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-rw-r--r--minrisk/minrisk_optimize.cc197
1 files changed, 0 insertions, 197 deletions
diff --git a/minrisk/minrisk_optimize.cc b/minrisk/minrisk_optimize.cc
deleted file mode 100644
index da8b5260..00000000
--- a/minrisk/minrisk_optimize.cc
+++ /dev/null
@@ -1,197 +0,0 @@
-#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"
-#include "entropy.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.)")
- ("temperature,T",po::value<double>()->default_value(0.0), "Temperature parameter for objective (>0 increases the entropy)")
- ("l1_strength,C",po::value<double>()->default_value(0.0), "L1 regularization strength")
- ("memory_buffers,M",po::value<unsigned>()->default_value(20), "Memory buffers used in LBFGS")
- ("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, const double temp) : training(tr), T(temp) {}
- double operator()(const vector<double>& x, double* g) const {
- fill(g, g + x.size(), 0.0);
- double obj = 0;
- double h = 0;
- for (unsigned i = 0; i < training.size(); ++i) {
- training::CandidateSetRisk risk(training[i], *metric);
- training::CandidateSetEntropy entropy(training[i]);
- SparseVector<double> tg, hg;
- double r = risk(x, &tg);
- double hh = entropy(x, &hg);
- h += hh;
- obj += r;
- for (SparseVector<double>::iterator it = tg.begin(); it != tg.end(); ++it)
- g[it->first] += it->second;
- if (T) {
- for (SparseVector<double>::iterator it = hg.begin(); it != hg.end(); ++it)
- g[it->first] += T * it->second;
- }
- }
- cerr << (1-(obj / training.size())) << " H=" << h << endl;
- return obj - T * h;
- }
- const vector<training::CandidateSet>& training;
- const double T; // temperature for entropy regularization
-};
-
-double LearnParameters(const vector<training::CandidateSet>& training,
- const double temp, // > 0 increases the entropy, < 0 decreases the entropy
- const double C1,
- const unsigned memory_buffers,
- vector<weight_t>* px) {
- RiskObjective obj(training, temp);
- LBFGS<RiskObjective> lbfgs(px, obj, memory_buffers, C1);
- lbfgs.MinimizeFunction();
- return 0;
-}
-
-#if 0
-struct FooLoss {
- double operator()(const vector<double>& x, double* g) const {
- fill(g, g + x.size(), 0.0);
- training::CandidateSet cs;
- training::CandidateSetEntropy cse(cs);
- cs.cs.resize(3);
- cs.cs[0].fmap.set_value(FD::Convert("F1"), -1.0);
- cs.cs[1].fmap.set_value(FD::Convert("F2"), 1.0);
- cs.cs[2].fmap.set_value(FD::Convert("F1"), 2.0);
- cs.cs[2].fmap.set_value(FD::Convert("F2"), 0.5);
- SparseVector<double> xx;
- double h = cse(x, &xx);
- cerr << cse(x, &xx) << endl; cerr << "G: " << xx << endl;
- for (SparseVector<double>::iterator i = xx.begin(); i != xx.end(); ++i)
- g[i->first] += i->second;
- return -h;
- }
-};
-#endif
-
-int main(int argc, char** argv) {
-#if 0
- training::CandidateSet cs;
- training::CandidateSetEntropy cse(cs);
- cs.cs.resize(3);
- cs.cs[0].fmap.set_value(FD::Convert("F1"), -1.0);
- cs.cs[1].fmap.set_value(FD::Convert("F2"), 1.0);
- cs.cs[2].fmap.set_value(FD::Convert("F1"), 2.0);
- cs.cs[2].fmap.set_value(FD::Convert("F2"), 0.5);
- FooLoss foo;
- vector<double> ww(FD::NumFeats()); ww[FD::Convert("F1")] = 1.0;
- LBFGS<FooLoss> lbfgs(&ww, foo, 100, 0.0);
- lbfgs.MinimizeFunction();
- return 1;
-#endif
- 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;
-
- 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());
-
- double c1 = conf["l1_strength"].as<double>();
- double temp = conf["temperature"].as<double>();
- unsigned m = conf["memory_buffers"].as<unsigned>();
- LearnParameters(kis, temp, c1, m, &weights);
- Weights::WriteToFile("-", weights);
- return 0;
-}
-
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