From 1b8181bf0d6e9137e6b9ccdbe414aec37377a1a9 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Sun, 18 Nov 2012 13:35:42 -0500 Subject: major restructure of the training code --- training/minrisk/minrisk_optimize.cc | 197 +++++++++++++++++++++++++++++++++++ 1 file changed, 197 insertions(+) create mode 100644 training/minrisk/minrisk_optimize.cc (limited to 'training/minrisk/minrisk_optimize.cc') diff --git a/training/minrisk/minrisk_optimize.cc b/training/minrisk/minrisk_optimize.cc new file mode 100644 index 00000000..da8b5260 --- /dev/null +++ b/training/minrisk/minrisk_optimize.cc @@ -0,0 +1,197 @@ +#include +#include +#include +#include + +#include +#include + +#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 >(), "[REQD] Reference translation (tokenized text)") + ("weights,w",po::value(), "[REQD] Weights files from current iterations") + ("input,i",po::value()->default_value("-"), "Input file to map (- is STDIN)") + ("evaluation_metric,m",po::value()->default_value("IBM_BLEU"), "Evaluation metric (ibm_bleu, koehn_bleu, nist_bleu, ter, meteor, etc.)") + ("temperature,T",po::value()->default_value(0.0), "Temperature parameter for objective (>0 increases the entropy)") + ("l1_strength,C",po::value()->default_value(0.0), "L1 regularization strength") + ("memory_buffers,M",po::value()->default_value(20), "Memory buffers used in LBFGS") + ("kbest_repository,R",po::value(), "Accumulate k-best lists from previous iterations (parameter is path to repository)") + ("kbest_size,k",po::value()->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 \n"; + flag = true; + } + if (!conf->count("weights")) { + cerr << "Please specify weights using -w \n"; + flag = true; + } + if (flag || conf->count("help")) { + cerr << dcmdline_options << endl; + exit(1); + } +} + +EvaluationMetric* metric = NULL; + +struct RiskObjective { + explicit RiskObjective(const vector& tr, const double temp) : training(tr), T(temp) {} + double operator()(const vector& 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 tg, hg; + double r = risk(x, &tg); + double hh = entropy(x, &hg); + h += hh; + obj += r; + for (SparseVector::iterator it = tg.begin(); it != tg.end(); ++it) + g[it->first] += it->second; + if (T) { + for (SparseVector::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; + const double T; // temperature for entropy regularization +}; + +double LearnParameters(const vector& training, + const double temp, // > 0 increases the entropy, < 0 decreases the entropy + const double C1, + const unsigned memory_buffers, + vector* px) { + RiskObjective obj(training, temp); + LBFGS lbfgs(px, obj, memory_buffers, C1); + lbfgs.MinimizeFunction(); + return 0; +} + +#if 0 +struct FooLoss { + double operator()(const vector& 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 xx; + double h = cse(x, &xx); + cerr << cse(x, &xx) << endl; cerr << "G: " << xx << endl; + for (SparseVector::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 ww(FD::NumFeats()); ww[FD::Convert("F1")] = 1.0; + LBFGS 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(); + + metric = EvaluationMetric::Instance(evaluation_metric); + DocumentScorer ds(metric, conf["reference"].as >()); + cerr << "Loaded " << ds.size() << " references for scoring with " << evaluation_metric << endl; + + Hypergraph hg; + string last_file; + ReadFile in_read(conf["input"].as()); + string kbest_repo; + if (conf.count("kbest_repository")) { + kbest_repo = conf["kbest_repository"].as(); + MkDirP(kbest_repo); + } + istream &in=*in_read.stream(); + const unsigned kbest_size = conf["kbest_size"].as(); + vector weights; + const string weightsf = conf["weights"].as(); + 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 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 temp = conf["temperature"].as(); + unsigned m = conf["memory_buffers"].as(); + LearnParameters(kis, temp, c1, m, &weights); + Weights::WriteToFile("-", weights); + return 0; +} + -- cgit v1.2.3