major restructure of the training code

1 files changed, 197 insertions, 0 deletions

diff --git a/training/minrisk/minrisk_optimize.cc b/training/minrisk/minrisk_optimize.cc
new file mode 100644
index 00000000..da8b5260
--- /dev/null
+++ b/training/minrisk/minrisk_optimize.cc
@@ -0,0 +1,197 @@
+#include <sstream>
+#include <iostream>
+#include <vector>
+#include <limits>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "liblbfgs/lbfgs++.h"
+#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"
+#include "risk.h"
+#include "entropy.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.)")
+        ("temperature,T",po::value<double>()->default_value(0.0), "Temperature parameter for objective (>0 increases the entropy)")
+        ("l1_strength,C",po::value<double>()->default_value(0.0), "L1 regularization strength")
+        ("memory_buffers,M",po::value<unsigned>()->default_value(20), "Memory buffers used in LBFGS")
+        ("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")
+        ("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);
+  }
+}
+
+EvaluationMetric* metric = NULL;
+
+struct RiskObjective {
+  explicit RiskObjective(const vector<training::CandidateSet>& tr, const double temp) : training(tr), T(temp) {}
+  double operator()(const vector<double>& x, double* g) const {
+    fill(g, g + x.size(), 0.0);
+    double obj = 0;
+    double h = 0;
+    for (unsigned i = 0; i < training.size(); ++i) {
+      training::CandidateSetRisk risk(training[i], *metric);
+      training::CandidateSetEntropy entropy(training[i]);
+      SparseVector<double> tg, hg;
+      double r = risk(x, &tg);
+      double hh = entropy(x, &hg);
+      h += hh;
+      obj += r;
+      for (SparseVector<double>::iterator it = tg.begin(); it != tg.end(); ++it)
+        g[it->first] += it->second;
+      if (T) {
+        for (SparseVector<double>::iterator it = hg.begin(); it != hg.end(); ++it)
+          g[it->first] += T * it->second;
+      }
+    }
+    cerr << (1-(obj / training.size())) << "  H=" << h << endl;
+    return obj - T * h;
+  }
+  const vector<training::CandidateSet>& training;
+  const double T; // temperature for entropy regularization
+};  
+
+double LearnParameters(const vector<training::CandidateSet>& training,
+                       const double temp, // > 0 increases the entropy, < 0 decreases the entropy
+                       const double C1,
+                       const unsigned memory_buffers,
+                       vector<weight_t>* px) {
+  RiskObjective obj(training, temp);
+  LBFGS<RiskObjective> lbfgs(px, obj, memory_buffers, C1);
+  lbfgs.MinimizeFunction();
+  return 0;
+}
+
+#if 0
+struct FooLoss {
+  double operator()(const vector<double>& x, double* g) const {
+    fill(g, g + x.size(), 0.0);
+    training::CandidateSet cs;
+    training::CandidateSetEntropy cse(cs);
+    cs.cs.resize(3);
+    cs.cs[0].fmap.set_value(FD::Convert("F1"), -1.0);
+    cs.cs[1].fmap.set_value(FD::Convert("F2"), 1.0);
+    cs.cs[2].fmap.set_value(FD::Convert("F1"), 2.0);
+    cs.cs[2].fmap.set_value(FD::Convert("F2"), 0.5);
+    SparseVector<double> xx;
+    double h = cse(x, &xx);
+    cerr << cse(x, &xx) << endl; cerr << "G: " << xx << endl;
+    for (SparseVector<double>::iterator i = xx.begin(); i != xx.end(); ++i)
+      g[i->first] += i->second;
+    return -h;
+  }
+};
+#endif
+
+int main(int argc, char** argv) {
+#if 0
+  training::CandidateSet cs;
+  training::CandidateSetEntropy cse(cs);
+  cs.cs.resize(3);
+  cs.cs[0].fmap.set_value(FD::Convert("F1"), -1.0);
+  cs.cs[1].fmap.set_value(FD::Convert("F2"), 1.0);
+  cs.cs[2].fmap.set_value(FD::Convert("F1"), 2.0);
+  cs.cs[2].fmap.set_value(FD::Convert("F2"), 0.5);
+  FooLoss foo;
+  vector<double> ww(FD::NumFeats()); ww[FD::Convert("F1")] = 1.0;
+  LBFGS<FooLoss> lbfgs(&ww, foo, 100, 0.0);
+  lbfgs.MinimizeFunction();
+  return 1;
+#endif
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
+  const string evaluation_metric = conf["evaluation_metric"].as<string>();
+
+  metric = EvaluationMetric::Instance(evaluation_metric);
+  DocumentScorer ds(metric, conf["reference"].as<vector<string> >());
+  cerr << "Loaded " << ds.size() << " references for scoring with " << evaluation_metric << endl;
+
+  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>();
+  vector<weight_t> weights;
+  const string weightsf = conf["weights"].as<string>();
+  Weights::InitFromFile(weightsf, &weights);
+  double t = 0;
+  for (unsigned i = 0; i < weights.size(); ++i)
+    t += weights[i] * weights[i];
+  if (t > 0) {
+    for (unsigned i = 0; i < weights.size(); ++i)
+      weights[i] /= sqrt(t);
+  }
+  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";
+  weights.resize(FD::NumFeats());
+
+  double c1 = conf["l1_strength"].as<double>();
+  double temp = conf["temperature"].as<double>();
+  unsigned m = conf["memory_buffers"].as<unsigned>();
+  LearnParameters(kis, temp, c1, m, &weights);
+  Weights::WriteToFile("-", weights);
+  return 0;
+}
+