1 files changed, 168 insertions, 0 deletions
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 <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;
+}
+