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-rw-r--r--training/rampion/rampion_cccp.cc168
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diff --git a/training/rampion/rampion_cccp.cc b/training/rampion/rampion_cccp.cc
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+++ b/training/rampion/rampion_cccp.cc
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+#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;
+}
+