major restructure of the training code

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;
-}
-