Added oracle forest rescoring

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@254 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 146 insertions, 8 deletions

diff --git a/decoder/cdec.cc b/decoder/cdec.cc
index b6cc6f66..5f06b0c8 100644
--- a/decoder/cdec.cc
+++ b/decoder/cdec.cc
@@ -32,6 +32,7 @@
 #include "inside_outside.h"
 #include "exp_semiring.h"
 #include "sentence_metadata.h"
+#include "../vest/scorer.h"
 
 using namespace std;
 using namespace std::tr1;
@@ -143,7 +144,9 @@ void InitCommandLine(int argc, char** argv, po::variables_map* confp) {
         ("pb_max_distortion,D", po::value<int>()->default_value(4), "Phrase-based decoder: maximum distortion")
         ("cll_gradient,G","Compute conditional log-likelihood gradient and write to STDOUT (src & ref required)")
         ("crf_uniform_empirical", "If there are multple references use (i.e., lattice) a uniform distribution rather than posterior weighting a la EM")
-        ("feature_expectations","Write feature expectations for all features in chart (**OBJ** will be the partition)")
+    ("get_oracle_forest,OO", "Calculate rescored hypregraph using approximate BLEU scoring of rules")
+    ("feature_expectations","Write feature expectations for all features in chart (**OBJ** will be the partition)")
+    ("references,R", po::value<vector<string> >(), "Translation reference files")    
         ("vector_format",po::value<string>()->default_value("b64"), "Sparse vector serialization format for feature expectations or gradients, includes (text or b64)")
         ("combine_size,C",po::value<int>()->default_value(1), "When option -G is used, process this many sentence pairs before writing the gradient (1=emit after every sentence pair)")
         ("forest_output,O",po::value<string>(),"Directory to write forests to")
@@ -258,16 +261,30 @@ void MaxTranslationSample(Hypergraph* hg, const int samples, const int k) {
 }
 
 // TODO decoder output should probably be moved to another file
-void DumpKBest(const int sent_id, const Hypergraph& forest, const int k, const bool unique) {
+void DumpKBest(const int sent_id, const Hypergraph& forest, const int k, const bool unique, const char *kbest_out_filename_, float doc_src_length, float tmp_src_length, const DocScorer &ds, Score* doc_score) {
 cerr << "In kbest\n";
+
+ ofstream kbest_out;
+ kbest_out.open(kbest_out_filename_);
+ cerr << "Output kbest to " << kbest_out_filename_;
+ 
+ //add length (f side) src length of this sentence to the psuedo-doc src length count
+ float curr_src_length = doc_src_length + tmp_src_length;
+
  if (unique) {
     KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique> kbest(forest, k);
     for (int i = 0; i < k; ++i) {
       const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique>::Derivation* d =
         kbest.LazyKthBest(forest.nodes_.size() - 1, i);
       if (!d) break;
-      cout << sent_id << " ||| " << TD::GetString(d->yield) << " ||| "
-           << d->feature_values << " ||| " << log(d->score) << endl;
+      //calculate score in context of psuedo-doc
+      Score* sentscore = ds[sent_id]->ScoreCandidate(d->yield);
+      sentscore->PlusEquals(*doc_score,float(1));
+      float bleu = curr_src_length * sentscore->ComputeScore();
+      kbest_out << sent_id << " ||| " << TD::GetString(d->yield) << " ||| "
+		<< d->feature_values << " ||| " << log(d->score) << " ||| " << bleu << endl;
+      // cout << sent_id << " ||| " << TD::GetString(d->yield) << " ||| "
+      //     << d->feature_values << " ||| " << log(d->score) << endl;
     }
   } else {
     KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, k);
@@ -498,6 +515,48 @@ int main(int argc, char** argv) {
   const bool kbest = conf.count("k_best");
   const bool unique_kbest = conf.count("unique_k_best");
   const bool crf_uniform_empirical = conf.count("crf_uniform_empirical");
+  const bool get_oracle_forest = conf.count("get_oracle_forest");
+
+  /*Oracle Extraction Prep*/
+  vector<const FeatureFunction*> oracle_model_ffs;
+  vector<double> oracle_feature_weights;
+  shared_ptr<FeatureFunction> oracle_pff;
+  if(get_oracle_forest)      {
+    
+    /*Add feature for oracle rescoring */
+    string ff, param;
+    ff="BLEUModel";
+    //pass the location of the references file via param to BLEUModel
+    for(int kk=0;kk < conf["references"].as<vector<string> >().size();kk++)
+      {
+	param =  param + " " + conf["references"].as<vector<string> >()[kk];
+      }       
+    cerr << "Feature: " << ff << "->" << param << endl;
+    oracle_pff = global_ff_registry->Create(ff,param);
+    if (!oracle_pff) { exit(1); }
+    oracle_model_ffs.push_back(oracle_pff.get());	
+    oracle_feature_weights.push_back(1.0);
+
+  }
+
+  ModelSet oracle_models(oracle_feature_weights, oracle_model_ffs); 
+
+  const string loss_function3 = "IBM_BLEU_3";
+  ScoreType type3 = ScoreTypeFromString(loss_function3);
+  const DocScorer ds(type3, conf["references"].as<vector<string> >(), "");
+  cerr << "Loaded " << ds.size() << " references for scoring with " << loss_function3 << endl;
+  
+    
+  std::ostringstream kbest_string_stream;
+  Score* doc_score=NULL;
+  float doc_src_length=0;
+  float tmp_src_length=0; 
+  int oracle_doc_size= 10;   //used for scaling/weighting oracle doc
+  float scale_oracle=  1-float(1)/oracle_doc_size;
+
+  /*End Oracle Extraction Prep*/
+
+
   shared_ptr<WriteFile> extract_file;
   if (conf.count("extract_rules"))
     extract_file.reset(new WriteFile(str("extract_rules",conf)));
@@ -610,6 +669,87 @@ int main(int argc, char** argv) {
 
     maybe_prune(forest,conf,"beam_prune","density_prune","+LM",srclen);
 
+    vector<WordID> trans;
+    ViterbiESentence(forest, &trans);
+    
+    /*Oracle Rescoring*/
+    if(get_oracle_forest)
+      {
+	Timer t("Forest Oracle rescoring:");
+	vector<WordID> model_trans;
+	model_trans = trans;
+
+	trans=model_trans;
+	Score* sentscore = ds[sent_id]->ScoreCandidate(model_trans);
+	//initilize psuedo-doc vector to 1 counts
+	if (!doc_score) { doc_score = sentscore->GetOne(); }
+	double bleu_scale_ = doc_src_length * doc_score->ComputeScore();
+	tmp_src_length = smeta.GetSourceLength();
+	smeta.SetScore(doc_score);
+	smeta.SetDocLen(doc_src_length);
+	smeta.SetDocScorer(&ds); 
+	
+	feature_weights[0]=1.0;	
+
+	kbest_string_stream << conf["forest_output"].as<string>() << "/kbest_model" << "." << sent_id;
+	DumpKBest(sent_id, forest, 10, true, kbest_string_stream.str().c_str(), doc_src_length, tmp_src_length, ds, doc_score);
+	kbest_string_stream.str("");
+
+
+	forest.SortInEdgesByEdgeWeights();
+	Hypergraph lm_forest;
+	const IntersectionConfiguration inter_conf_oracle(0, 0);
+	cerr << "Going to call Apply Model " << endl;
+	ApplyModelSet(forest,
+		      smeta,
+		      oracle_models,
+		      inter_conf_oracle,
+		      &lm_forest);
+	
+	forest.swap(lm_forest);
+	forest.Reweight(feature_weights);
+	forest.SortInEdgesByEdgeWeights();
+	vector<WordID> oracle_trans;
+
+	ViterbiESentence(forest, &oracle_trans);
+	cerr << "  +Oracle BLEU forest (nodes/edges): " << forest.nodes_.size() << '/' << forest.edges_.size() << endl;
+	cerr << "  +Oracle BLEU (paths): " << forest.NumberOfPaths() << endl;
+	cerr << "  +Oracle BLEU Viterbi: " << TD::GetString(oracle_trans) << endl;  
+      
+	//compute kbest for oracle
+	kbest_string_stream << conf["forest_output"].as<string>() <<"/kbest_oracle" << "." << sent_id;
+	DumpKBest(sent_id, forest, 10, true, kbest_string_stream.str().c_str(), doc_src_length, tmp_src_length, ds, doc_score);
+	kbest_string_stream.str("");
+	
+	
+	//reweight the model with -1 for the BLEU feature to compute k-best list for negative examples
+	feature_weights[0]=-1.0;
+	forest.Reweight(feature_weights);
+	forest.SortInEdgesByEdgeWeights();
+	vector<WordID> neg_trans;
+	ViterbiESentence(forest, &neg_trans);
+	cerr << "  -Oracle BLEU forest (nodes/edges): " << forest.nodes_.size() << '/' << forest.edges_.size() << endl;
+	cerr << "  -Oracle BLEU (paths): " << forest.NumberOfPaths() << endl;
+	cerr << "  -Oracle BLEU Viterbi: " << TD::GetString(neg_trans) << endl;  
+	
+	//compute kbest for negative
+	kbest_string_stream << conf["forest_output"].as<string>() << "/kbest_negative" << "." << sent_id;
+	DumpKBest(sent_id, forest, 10, true, kbest_string_stream.str().c_str(), doc_src_length, tmp_src_length,ds,  doc_score);
+	kbest_string_stream.str("");
+		
+	//Add 1-best translation (trans) to psuedo-doc vectors
+	doc_score->PlusEquals(*sentscore, scale_oracle);
+	delete sentscore;
+	
+	doc_src_length = (doc_src_length + tmp_src_length) * scale_oracle;
+	
+	
+	string details;
+	doc_score->ScoreDetails(&details);
+	cerr << "SCALED SCORE: " << bleu_scale_ << "DOC BLEU " << doc_score->ComputeScore() << " " <<details << endl;
+      }
+
+
     if (conf.count("forest_output") && !has_ref) {
       ForestWriter writer(str("forest_output",conf), sent_id);
       if (FileExists(writer.fname_)) {
@@ -632,11 +772,9 @@ int main(int argc, char** argv) {
     if (sample_max_trans) {
       MaxTranslationSample(&forest, sample_max_trans, conf.count("k_best") ? conf["k_best"].as<int>() : 0);
     } else {
-      vector<WordID> trans;
-      ViterbiESentence(forest, &trans);
-
+      
       if (kbest) {
-        DumpKBest(sent_id, forest, conf["k_best"].as<int>(), unique_kbest);
+        DumpKBest(sent_id, forest, conf["k_best"].as<int>(), unique_kbest,"", doc_src_length, tmp_src_length, ds,  doc_score);
       } else if (csplit_output_plf) {
         cout << HypergraphIO::AsPLF(forest, false) << endl;
       } else {