From ba23531ee5ba0c281ddfe1b40e5148ae2d94c277 Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cs.cmu.edu>
Date: Sun, 24 Jun 2012 22:30:50 -0400
Subject: minimum risk training, not completely ready for primetime

---
 minrisk/minrisk_optimize.cc | 141 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 141 insertions(+)
 create mode 100644 minrisk/minrisk_optimize.cc

(limited to 'minrisk/minrisk_optimize.cc')

diff --git a/minrisk/minrisk_optimize.cc b/minrisk/minrisk_optimize.cc
new file mode 100644
index 00000000..5096acc1
--- /dev/null
+++ b/minrisk/minrisk_optimize.cc
@@ -0,0 +1,141 @@
+#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"
+
+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")
+        ("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) : training(tr) {}
+  double operator()(const vector<double>& x, double* g) const {
+    fill(g, g + x.size(), 0.0);
+    double obj = 0;
+    for (unsigned i = 0; i < training.size(); ++i) {
+      training::CandidateSetRisk risk(training[i], *metric);
+      SparseVector<double> tg;
+      double r = risk(x, &tg);
+      obj += r;
+      for (SparseVector<double>::iterator it = tg.begin(); it != tg.end(); ++it)
+        g[it->first] += it->second;
+    }
+    cerr << (1-(obj / training.size())) << endl;
+    return obj;
+  }
+  const vector<training::CandidateSet>& training;
+};  
+
+double LearnParameters(const vector<training::CandidateSet>& training,
+                       const double C1,
+                       const unsigned memory_buffers,
+                       vector<weight_t>* px) {
+  RiskObjective obj(training);
+  LBFGS<RiskObjective> lbfgs(px, obj, memory_buffers, C1);
+  lbfgs.MinimizeFunction();
+  return 0;
+}
+
+// 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>();
+
+  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>();
+  vector<weight_t> weights;
+  const string weightsf = conf["weights"].as<string>();
+  Weights::InitFromFile(weightsf, &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";
+  weights.resize(FD::NumFeats());
+
+  LearnParameters(kis, 0.0, 100, &weights);
+  Weights::WriteToFile("-", weights);
+  return 0;
+}
+
-- 
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