kbest, ksampler refactoring

1 files changed, 24 insertions, 24 deletions

diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index a70ca2f1..ad1ab7b7 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -10,11 +10,11 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     ("output",         po::value<string>()->default_value("-"),       "output weights file (or VOID)")
     ("input_weights",  po::value<string>(),       "input weights file (e.g. from previous iteration)")
     ("decoder_config", po::value<string>(),                             "configuration file for cdec")
-    ("ksamples",       po::value<size_t>()->default_value(100), "size of kbest or sample from forest")
+    ("k",              po::value<size_t>()->default_value(100), "size of kbest or sample from forest")
     ("sample_from",    po::value<string>()->default_value("kbest"),  "where to get translations from")
     ("filter",         po::value<string>()->default_value("unique"),              "filter kbest list")
     ("pair_sampling",  po::value<string>()->default_value("all"),    "how to sample pairs: all, rand")
-    ("ngrams",         po::value<size_t>()->default_value(3),                          "N for Ngrams")
+    ("N",              po::value<size_t>()->default_value(3),                          "N for Ngrams")
     ("epochs",         po::value<size_t>()->default_value(2),                     "# of iterations T") 
     ("scorer",         po::value<string>()->default_value("stupid_bleu"),            "scoring metric")
     ("stop_after",     po::value<size_t>()->default_value(0),          "stop after X input sentences")
@@ -75,8 +75,8 @@ main(int argc, char** argv)
     hstreaming = true;
     quiet = true;
   }
-  const size_t k = cfg["ksamples"].as<size_t>();
-  const size_t N = cfg["ngrams"].as<size_t>(); 
+  const size_t k = cfg["k"].as<size_t>();
+  const size_t N = cfg["N"].as<size_t>(); 
   const size_t T = cfg["epochs"].as<size_t>();
   const size_t stop_after = cfg["stop_after"].as<size_t>();
   const string filter_type = cfg["filter"].as<string>();
@@ -96,7 +96,7 @@ main(int argc, char** argv)
 
   MT19937 rng; // random number generator
   // setup decoder observer
-  HypoSampler* observer;
+  HypSampler* observer;
   if (sample_from == "kbest") {
     observer = dynamic_cast<KBestGetter*>(new KBestGetter(k, filter_type));
   } else {
@@ -274,45 +274,45 @@ main(int argc, char** argv)
       decoder.Decode(src_str_buf[ii], observer);
     }
 
-    Samples* samples = observer->GetSamples();
+    vector<ScoredHyp>* samples = observer->GetSamples();
 
     // (local) scoring
     if (t > 0) ref_ids = ref_ids_buf[ii];
     score_t score = 0.;
-    for (size_t i = 0; i < samples->GetSize(); i++) {
-      NgramCounts counts = make_ngram_counts(ref_ids, samples->sents[i], N);
+    for (size_t i = 0; i < samples->size(); i++) {
+      NgramCounts counts = make_ngram_counts(ref_ids, (*samples)[i].w, N);
       if (scorer_str == "approx_bleu") {
         size_t hyp_len = 0;
         if (i == 0) { // 'context of 1best translations'
           global_counts  += counts;
-          global_hyp_len += samples->sents[i].size();
+          global_hyp_len += (*samples)[i].w.size();
           global_ref_len += ref_ids.size();
           counts.reset();
         } else {
-            hyp_len = samples->sents[i].size();
+            hyp_len = (*samples)[i].w.size();
         }
-        NgramCounts counts_tmp = global_counts + counts;
-        score = .9 * scorer(counts_tmp,
+        NgramCounts _c = global_counts + counts;
+        score = .9 * scorer(_c,
                             global_ref_len,
                             global_hyp_len + hyp_len, N, bleu_weights);
       } else {
         score = scorer(counts,
                        ref_ids.size(),
-                       samples->sents[i].size(), N, bleu_weights);
+                       (*samples)[i].w.size(), N, bleu_weights);
       }
 
-      samples->scores.push_back(score);
+      (*samples)[i].score = (score);
 
       if (i == 0) {
         score_sum += score;
-        model_sum += samples->model_scores[i];
+        model_sum += (*samples)[i].model;
       }
 
       if (verbose) {
         if (i == 0) cout << "'" << TD::GetString(ref_ids) << "' [ref]" << endl;
-        cout << _p5 << _np << "[hyp " << i << "] " << "'" << TD::GetString(samples->sents[i]) << "'";
-        cout << " [SCORE=" << score << ",model="<< samples->model_scores[i] << "]" << endl;
-        cout << samples->feats[i] << endl;
+        cout << _p5 << _np << "[hyp " << i << "] " << "'" << TD::GetString((*samples)[i].w) << "'";
+        cout << " [SCORE=" << score << ",model="<< (*samples)[i].model << "]" << endl;
+        cout << (*samples)[i].f << endl;
       }
     } // sample/scoring loop
 
@@ -321,18 +321,18 @@ main(int argc, char** argv)
 //////////////////////////////////////////////////////////
     // UPDATE WEIGHTS
     if (!noup) {
-      vector<Pair> pairs;
+      vector<pair<ScoredHyp,ScoredHyp> > pairs;
       if (pair_sampling == "all")
         sample_all_pairs(samples, pairs);
       if (pair_sampling == "rand")
         sample_rand_pairs(samples, pairs, &rng);
        
-      for (vector<Pair>::iterator ti = pairs.begin();
+      for (vector<pair<ScoredHyp,ScoredHyp> >::iterator ti = pairs.begin();
             ti != pairs.end(); ti++) {
 
         SparseVector<double> dv;
-        if (ti->first_score - ti->second_score < 0) {
-          dv = ti->second - ti->first;
+        if (ti->first.score - ti->second.score < 0) {
+          dv = ti->second.f - ti->first.f;
       //} else {
         //dv = ti->first - ti->second;
       //}
@@ -344,14 +344,14 @@ main(int argc, char** argv)
           lambdas += dv * eta;
 
           if (verbose) {
-            cout << "{{ f("<< ti->first_rank <<") > f(" << ti->second_rank << ") but g(i)="<< ti->first_score <<" < g(j)="<< ti->second_score << " so update" << endl;
+            /*cout << "{{ f("<< ti->first_rank <<") > f(" << ti->second_rank << ") but g(i)="<< ti->first_score <<" < g(j)="<< ti->second_score << " so update" << endl;
             cout << " i  " << TD::GetString(samples->sents[ti->first_rank]) << endl;
             cout << "    " << samples->feats[ti->first_rank] << endl;
             cout << " j  " << TD::GetString(samples->sents[ti->second_rank]) << endl;
             cout << "    " << samples->feats[ti->second_rank] << endl; 
             cout << " diff vec: " << dv << endl;
             cout << " lambdas after update: " << lambdas << endl;
-            cout << "}}" << endl;
+            cout << "}}" << endl;*/
           }
         } else {
           //SparseVector<double> reg;