src language LM phrase scoring

1 files changed, 93 insertions, 0 deletions

diff --git a/training/augment_grammar.cc b/training/augment_grammar.cc
new file mode 100644
index 00000000..9b7fc7be
--- /dev/null
+++ b/training/augment_grammar.cc
@@ -0,0 +1,93 @@
+#include <iostream>
+#include <vector>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "rule_lexer.h"
+#include "trule.h"
+#include "filelib.h"
+#include "tdict.h"
+#include "lm/model.hh"
+#include "lm/enumerate_vocab.hh"
+#include "wordid.h"
+
+namespace po = boost::program_options;
+using namespace std;
+
+vector<lm::WordIndex> word_map;
+lm::ngram::ProbingModel* ngram;
+struct VMapper : public lm::ngram::EnumerateVocab {
+  VMapper(vector<lm::WordIndex>* out) : out_(out), kLM_UNKNOWN_TOKEN(0) { out_->clear(); }
+  void Add(lm::WordIndex index, const StringPiece &str) {
+    const WordID cdec_id = TD::Convert(str.as_string());
+    if (cdec_id >= out_->size())
+      out_->resize(cdec_id + 1, kLM_UNKNOWN_TOKEN);
+    (*out_)[cdec_id] = index;
+  }
+  vector<lm::WordIndex>* out_;
+  const lm::WordIndex kLM_UNKNOWN_TOKEN;
+};
+
+bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("source_lm,l",po::value<string>(),"Source language LM (KLM)")
+        ("add_shape_types,s", "Add rule shape types");
+  po::options_description clo("Command line options");
+  clo.add_options()
+        ("config", po::value<string>(), "Configuration file")
+        ("help,h", "Print this help message and exit");
+  po::options_description dconfig_options, dcmdline_options;
+  dconfig_options.add(opts);
+  dcmdline_options.add(opts).add(clo);
+  
+  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+  if (conf->count("config")) {
+    ifstream config((*conf)["config"].as<string>().c_str());
+    po::store(po::parse_config_file(config, dconfig_options), *conf);
+  }
+  po::notify(*conf);
+
+  if (conf->count("help")) {
+    cerr << "Usage " << argv[0] << " [OPTIONS]\n";
+    cerr << dcmdline_options << endl;
+    return false;
+  }
+  return true;
+}
+
+template <class Model> float Score(const vector<WordID>& str, const Model &model) {
+  typename Model::State state, out;
+  lm::FullScoreReturn ret;
+  float total = 0.0f;
+  state = model.NullContextState();
+
+  for (int i = 0; i < str.size(); ++i) {
+    lm::WordIndex vocab = ((str[i] < word_map.size() && str[i] > 0) ? word_map[str[i]] : 0);
+    ret = model.FullScore(state, vocab, out);
+    total += ret.prob;
+    state = out;
+  }
+  return total;
+}
+
+static void RuleHelper(const TRulePtr& new_rule, const unsigned int ctf_level, const TRulePtr& coarse_rule, void* extra) {
+  cout << *new_rule << " SrcLM=" << Score(new_rule->f_, *ngram) << endl;
+}
+
+int main(int argc, char** argv) {
+  po::variables_map conf;
+  if (!InitCommandLine(argc, argv, &conf)) return 1;
+  if (conf.count("source_lm")) {
+    lm::ngram::Config kconf;
+    VMapper vm(&word_map);
+    kconf.enumerate_vocab = &vm; 
+    ngram = new lm::ngram::ProbingModel(conf["source_lm"].as<string>().c_str(), kconf);
+    cerr << "Loaded " << (int)ngram->Order() << "-gram KenLM (MapSize=" << word_map.size() << ")\n";
+  } else { ngram = NULL; }
+  assert(ngram);
+  RuleLexer::ReadRules(&cin, &RuleHelper, NULL);
+  return 0;
+}
+