dtrain_net_interface output rules too

3 files changed, 10 insertions, 1 deletions

diff --git a/training/dtrain/dtrain_net_interface.cc b/training/dtrain/dtrain_net_interface.cc
index 01b110b4..f16b9304 100644
--- a/training/dtrain/dtrain_net_interface.cc
+++ b/training/dtrain/dtrain_net_interface.cc
@@ -28,6 +28,7 @@ main(int argc, char** argv)
   boost::split(dense_features, conf["dense_features"].as<string>(),
                boost::is_any_of(" "));
   const bool output_derivation = conf["output_derivation"].as<bool>();
+  const bool output_rules      = conf["output_rules"].as<bool>();
 
   // setup decoder
   register_feature_functions();
@@ -132,6 +133,9 @@ main(int argc, char** argv)
           } else {
             PrintWordIDVec((*samples)[0].w, os);
           }
+          if (output_rules) {
+            os << observer->GetViterbiRules() << endl;
+          }
           sock.send(os.str().c_str(), os.str().size()+1, 0);
           cerr << "[dtrain] done translating, looping again" << endl;
           continue;
diff --git a/training/dtrain/dtrain_net_interface.h b/training/dtrain/dtrain_net_interface.h
index 3c7665a2..816237c3 100644
--- a/training/dtrain/dtrain_net_interface.h
+++ b/training/dtrain/dtrain_net_interface.h
@@ -65,6 +65,7 @@ dtrain_net_init(int argc, char** argv, po::variables_map* conf)
     ("learning_rate,l",        po::value<weight_t>()->default_value(1.0),                     "learning rate")
     ("learning_rate_sparse,l", po::value<weight_t>()->default_value(1.0), "learning rate for sparse features")
     ("output_derivation,E",    po::bool_switch()->default_value(false),  "output derivation, not viterbi str")
+    ("output_rules,R",    po::bool_switch()->default_value(false),                        "also output rules")
     ("dense_features,D",       po::value<string>()->default_value("EgivenFCoherent SampleCountF CountEF MaxLexFgivenE MaxLexEgivenF IsSingletonF IsSingletonFE Glue WordPenalty PassThrough LanguageModel LanguageModel_OOV Shape_S01111_T11011 Shape_S11110_T11011 Shape_S11100_T11000 Shape_S01110_T01110 Shape_S01111_T01111 Shape_S01100_T11000 Shape_S10000_T10000 Shape_S11100_T11100 Shape_S11110_T11110 Shape_S11110_T11010 Shape_S01100_T11100 Shape_S01000_T01000 Shape_S01010_T01010 Shape_S01111_T01011 Shape_S01100_T01100 Shape_S01110_T11010 Shape_S11000_T11000 Shape_S11000_T01100 IsSupportedOnline ForceRule"),
                                                                                              "dense features")
     ("debug_output,d",   po::value<string>()->default_value(""),                      "file for debug output");
diff --git a/training/dtrain/sample_net_interface.h b/training/dtrain/sample_net_interface.h
index a2b5f87d..6d00e5d5 100644
--- a/training/dtrain/sample_net_interface.h
+++ b/training/dtrain/sample_net_interface.h
@@ -17,7 +17,7 @@ struct ScoredKbest : public DecoderObserver
   vector<Ngrams>* ref_ngs_;
   vector<size_t>* ref_ls_;
   bool dont_score;
-  string viterbiTreeStr_;
+  string viterbiTreeStr_, viterbiRules_;
 
   ScoredKbest(const size_t k, PerSentenceBleuScorer* scorer) :
     k_(k), scorer_(scorer), dont_score(false) {}
@@ -44,6 +44,9 @@ struct ScoredKbest : public DecoderObserver
       effective_sz_++;
       feature_count_ += h.f.size();
       viterbiTreeStr_ = hg->show_viterbi_tree(false);
+      ostringstream ss;
+      ViterbiRules(*hg, &ss);
+      viterbiRules_ = ss.str();
     }
   }
 
@@ -56,6 +59,7 @@ struct ScoredKbest : public DecoderObserver
   inline size_t GetFeatureCount() { return feature_count_; }
   inline size_t GetSize() { return effective_sz_; }
   inline string GetViterbiTreeStr() { return viterbiTreeStr_; }
+  inline string GetViterbiRules() { return viterbiRules_; }
 };
 
 } // namespace