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-rw-r--r--rampion/rampion_cccp.cc168
1 files changed, 0 insertions, 168 deletions
diff --git a/rampion/rampion_cccp.cc b/rampion/rampion_cccp.cc
deleted file mode 100644
index 1e36dc51..00000000
--- a/rampion/rampion_cccp.cc
+++ /dev/null
@@ -1,168 +0,0 @@
-#include <sstream>
-#include <iostream>
-#include <vector>
-#include <limits>
-
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#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<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.)")
- ("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")
- ("cccp_iterations,I", po::value<unsigned>()->default_value(10u), "CCCP iterations (T')")
- ("ssd_iterations,J", po::value<unsigned>()->default_value(5u), "Stochastic subgradient iterations (T'')")
- ("eta", po::value<double>()->default_value(1e-4), "Step size")
- ("regularization_strength,C", po::value<double>()->default_value(1.0), "L2 regularization strength")
- ("alpha,a", po::value<double>()->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 <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 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 <typename GainFunc>
-void CostAugmentedSearch(const GainFunc& gain,
- const training::CandidateSet& cs,
- const SparseVector<double>& w,
- double alpha,
- SparseVector<double>* fmap) {
- unsigned best_i = 0;
- double best = -numeric_limits<double>::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<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;
- double goodsign = -1;
- double badsign = -goodsign;
-
- 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>();
- const unsigned tp = conf["cccp_iterations"].as<unsigned>();
- const unsigned tpp = conf["ssd_iterations"].as<unsigned>();
- const double eta = conf["eta"].as<double>();
- const double reg = conf["regularization_strength"].as<double>();
- const double alpha = conf["alpha"].as<double>();
- SparseVector<weight_t> weights;
- {
- vector<weight_t> vweights;
- const string weightsf = conf["weights"].as<string>();
- Weights::InitFromFile(weightsf, &vweights);
- Weights::InitSparseVector(vweights, &weights);
- }
- 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";
-
- vector<SparseVector<weight_t> > goals(kis.size()); // f(x_i,y+,h+)
- SparseVector<weight_t> 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<weight_t> w;
- weights.init_vector(&w);
- Weights::WriteToFile("-", w);
- return 0;
-}
-
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