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/rampion/rampion_cccp.cc | 168 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 168 insertions(+) create mode 100644 training/rampion/rampion_cccp.cc (limited to 'training/rampion/rampion_cccp.cc') diff --git a/training/rampion/rampion_cccp.cc b/training/rampion/rampion_cccp.cc new file mode 100644 index 00000000..1e36dc51 --- /dev/null +++ b/training/rampion/rampion_cccp.cc @@ -0,0 +1,168 @@ +#include +#include +#include +#include + +#include +#include + +#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" + +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.)") + ("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") + ("cccp_iterations,I", po::value()->default_value(10u), "CCCP iterations (T')") + ("ssd_iterations,J", po::value()->default_value(5u), "Stochastic subgradient iterations (T'')") + ("eta", po::value()->default_value(1e-4), "Step size") + ("regularization_strength,C", po::value()->default_value(1.0), "L2 regularization strength") + ("alpha,a", po::value()->default_value(10.0), "Cost scale (alpha); alpha * [1-metric(y,y')]") + ("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); + } +} + +struct GainFunction { + explicit GainFunction(const EvaluationMetric* m) : metric(m) {} + float operator()(const SufficientStats& eval_feats) const { + float g = metric->ComputeScore(eval_feats); + if (!metric->IsErrorMetric()) g = 1 - g; + return g; + } + const EvaluationMetric* metric; +}; + +template +void CostAugmentedSearch(const GainFunc& gain, + const training::CandidateSet& cs, + const SparseVector& w, + double alpha, + SparseVector* fmap) { + unsigned best_i = 0; + double best = -numeric_limits::infinity(); + for (unsigned i = 0; i < cs.size(); ++i) { + double s = cs[i].fmap.dot(w) + alpha * gain(cs[i].eval_feats); + if (s > best) { + best = s; + best_i = i; + } + } + *fmap = cs[best_i].fmap; +} + + + +// runs lines 4--15 of rampion algorithm +int main(int argc, char** argv) { + po::variables_map conf; + InitCommandLine(argc, argv, &conf); + const string evaluation_metric = conf["evaluation_metric"].as(); + + EvaluationMetric* metric = EvaluationMetric::Instance(evaluation_metric); + DocumentScorer ds(metric, conf["reference"].as >()); + cerr << "Loaded " << ds.size() << " references for scoring with " << evaluation_metric << endl; + double goodsign = -1; + double badsign = -goodsign; + + 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(); + const unsigned tp = conf["cccp_iterations"].as(); + const unsigned tpp = conf["ssd_iterations"].as(); + const double eta = conf["eta"].as(); + const double reg = conf["regularization_strength"].as(); + const double alpha = conf["alpha"].as(); + SparseVector weights; + { + vector vweights; + const string weightsf = conf["weights"].as(); + Weights::InitFromFile(weightsf, &vweights); + Weights::InitSparseVector(vweights, &weights); + } + 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"; + + vector > goals(kis.size()); // f(x_i,y+,h+) + SparseVector fear; // f(x,y-,h-) + const GainFunction gain(metric); + for (unsigned iterp = 1; iterp <= tp; ++iterp) { + cerr << "CCCP Iteration " << iterp << endl; + for (unsigned i = 0; i < goals.size(); ++i) + CostAugmentedSearch(gain, kis[i], weights, goodsign * alpha, &goals[i]); + for (unsigned iterpp = 1; iterpp <= tpp; ++iterpp) { + cerr << " SSD Iteration " << iterpp << endl; + for (unsigned i = 0; i < goals.size(); ++i) { + CostAugmentedSearch(gain, kis[i], weights, badsign * alpha, &fear); + weights -= weights * (eta * reg / goals.size()); + weights += (goals[i] - fear) * eta; + } + } + } + vector w; + weights.init_vector(&w); + Weights::WriteToFile("-", w); + return 0; +} + -- cgit v1.2.3