1 files changed, 111 insertions, 0 deletions
diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc
new file mode 100644
index 00000000..b046cdea
--- /dev/null
+++ b/pro-train/mr_pro_map.cc
@@ -0,0 +1,111 @@
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "sampler.h"
+#include "filelib.h"
+#include "stringlib.h"
+#include "scorer.h"
+#include "inside_outside.h"
+#include "hg_io.h"
+#include "kbest.h"
+#include "viterbi.h"
+
+// This is Figure 4 (Algorithm Sampler) from Hopkins&May (2011)
+
+using namespace std;
+namespace po = boost::program_options;
+
+boost::shared_ptr<MT19937> rng;
+
+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)")
+        ("source,s",po::value<string>(), "Source file (ignored, except for AER)")
+        ("loss_function,l",po::value<string>()->default_value("ibm_bleu"), "Loss function being optimized")
+        ("input,i",po::value<string>()->default_value("-"), "Input file to map (- is STDIN)")
+        ("weights,w",po::value<string>(), "[REQD] Current weights file")
+        ("kbest_size,k",po::value<unsigned>()->default_value(1500u), "Top k-hypotheses to extract")
+        ("candidate_pairs,G", po::value<unsigned>()->default_value(5000u), "Number of pairs to sample per hypothesis (Gamma)")
+        ("best_pairs,X", po::value<unsigned>()->default_value(50u), "Number of pairs, ranked by magnitude of objective delta, to retain (Xi)")
+        ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
+        ("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 (flag || conf->count("help")) {
+    cerr << dcmdline_options << endl;
+    exit(1);
+  }
+}
+
+struct HypInfo {
+  HypInfo(const vector<WordID>& h, const SparseVector<double>& feats) : hyp(h), g_(-1), x(feats) {}
+  double g() {
+    return g_;
+  }
+ private:
+  int sent_id;
+  vector<WordID> hyp;
+  double g_;
+ public:
+  SparseVector<double> x;
+};
+
+int main(int argc, char** argv) {
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
+  if (conf.count("random_seed"))
+    rng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
+  else
+    rng.reset(new MT19937);
+  const string loss_function = conf["loss_function"].as<string>();
+  ScoreType type = ScoreTypeFromString(loss_function);
+  DocScorer ds(type, conf["reference"].as<vector<string> >(), conf["source"].as<string>());
+  cerr << "Loaded " << ds.size() << " references for scoring with " << loss_function << endl;
+  Hypergraph hg;
+  string last_file;
+  ReadFile in_read(conf["input"].as<string>());
+  istream &in=*in_read.stream();
+  const unsigned kbest_size = conf["kbest_size"].as<unsigned>();
+  const unsigned gamma = conf["candidate_pairs"].as<unsigned>();
+  const unsigned xi = conf["best_pairs"].as<unsigned>();
+  while(in) {
+    string line;
+    getline(in, line);
+    if (line.empty()) continue;
+    istringstream is(line);
+    int sent_id;
+    string file;
+    // path-to-file (JSON) sent_id
+    is >> file >> sent_id;
+    ReadFile rf(file);
+    HypergraphIO::ReadFromJSON(rf.stream(), &hg);
+    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(hg, kbest_size);
+
+    vector<HypInfo> J_i;
+    for (int i = 0; i < kbest_size; ++i) {
+      const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
+        kbest.LazyKthBest(hg.nodes_.size() - 1, i);
+      if (!d) break;
+      float sentscore = ds[sent_id]->ScoreCandidate(d->yield)->ComputeScore();
+      // if (invert_score) sentscore *= -1.0;
+      // cerr << TD::GetString(d->yield) << " ||| " << d->score << " ||| " << sentscore << endl;
+      d->feature_values;
+      sentscore;
+    }
+  }
+  return 0;
+}
+