kbest, ksampler refactoring

5 files changed, 87 insertions, 105 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;
diff --git a/dtrain/kbestget.h b/dtrain/kbestget.h
index 79201182..403384de 100644
--- a/dtrain/kbestget.h
+++ b/dtrain/kbestget.h
@@ -7,28 +7,27 @@ namespace dtrain
 {
 
 
-struct Samples
+struct ScoredHyp
 {
-  vector<SparseVector<double> > feats;
-  vector<vector<WordID> > sents;
-  vector<double> model_scores;
-  vector<double> scores;
-  size_t GetSize() { return sents.size(); }
+  vector<WordID> w;
+  SparseVector<double> f;
+  score_t model;
+  score_t score;
 };
 
-struct HypoSampler : public DecoderObserver
+struct HypSampler : public DecoderObserver
 {
-  virtual Samples* GetSamples() {}
+  virtual vector<ScoredHyp>* GetSamples() {}
 };
 
-struct KBestGetter : public HypoSampler
+struct KBestGetter : public HypSampler
 {
   const size_t k_;
-  const string filter_type;
-  Samples s;
+  const string filter_type_;
+  vector<ScoredHyp> s_;
 
   KBestGetter(const size_t k, const string filter_type) :
-    k_(k), filter_type(filter_type) {}
+    k_(k), filter_type_(filter_type) {}
 
   virtual void
   NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
@@ -36,14 +35,14 @@ struct KBestGetter : public HypoSampler
     KBest(*hg);
   }
 
-  Samples* GetSamples() { return &s; }
+  vector<ScoredHyp>* GetSamples() { return &s_; }
 
   void
   KBest(const Hypergraph& forest)
   {
-    if (filter_type == "unique") {
+    if (filter_type_ == "unique") {
       KBestUnique(forest);
-    } else if (filter_type == "no") {
+    } else if (filter_type_ == "no") {
       KBestNoFilter(forest);
     }
   }
@@ -51,36 +50,34 @@ struct KBestGetter : public HypoSampler
   void
   KBestUnique(const Hypergraph& forest)
   {
-    s.sents.clear();
-    s.feats.clear();
-    s.model_scores.clear();
-    s.scores.clear();
+    s_.clear();
     KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique, prob_t, EdgeProb> kbest(forest, k_);
     for (size_t i = 0; i < k_; ++i) {
       const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique, prob_t, EdgeProb>::Derivation* d =
             kbest.LazyKthBest(forest.nodes_.size() - 1, i);
       if (!d) break;
-      s.sents.push_back(d->yield);
-      s.feats.push_back(d->feature_values);
-      s.model_scores.push_back(log(d->score));
+      ScoredHyp h;
+      h.w = d->yield;
+      h.f = d->feature_values;
+      h.model = log(d->score);
+      s_.push_back(h);
     }
   }
 
   void
   KBestNoFilter(const Hypergraph& forest)
   {
-    s.sents.clear();
-    s.feats.clear();
-    s.model_scores.clear();
-    s.scores.clear();
+    s_.clear();
     KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, k_);
     for (size_t i = 0; i < k_; ++i) {
       const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
             kbest.LazyKthBest(forest.nodes_.size() - 1, i);
       if (!d) break;
-      s.sents.push_back(d->yield);
-      s.feats.push_back(d->feature_values);
-      s.model_scores.push_back(log(d->score));
+      ScoredHyp h;
+      h.w = d->yield;
+      h.f = d->feature_values;
+      h.model = log(d->score);
+      s_.push_back(h);
     }
   }
 };
diff --git a/dtrain/ksampler.h b/dtrain/ksampler.h
index ac88b643..bbe2b402 100644
--- a/dtrain/ksampler.h
+++ b/dtrain/ksampler.h
@@ -13,34 +13,33 @@ namespace dtrain
  * KSampler
  *
  */
-struct KSampler : public HypoSampler
+struct KSampler : public HypSampler
 {
   const size_t k_;
-  Samples s;
-  MT19937* rng;
+  vector<ScoredHyp> s_;
+  MT19937* prng_;
 
-  explicit KSampler( const size_t k, MT19937* prng ) :
-    k_(k), rng(prng) {}
+  explicit KSampler(const size_t k, MT19937* prng) :
+    k_(k), prng_(prng) {}
 
   virtual void
-  NotifyTranslationForest( const SentenceMetadata& smeta, Hypergraph* hg )
+  NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
   {
-    Sample( *hg );
+    Sample(*hg);
   }
 
-  Samples* GetSamples() { return &s; }
+  vector<ScoredHyp>* GetSamples() { return &s_; }
 
-  void Sample( const Hypergraph& forest ) {
-    s.sents.clear();
-    s.feats.clear();
-    s.model_scores.clear();
-    s.scores.clear();
+  void Sample(const Hypergraph& forest) {
+    s_.clear();
     std::vector<HypergraphSampler::Hypothesis> samples;
-    HypergraphSampler::sample_hypotheses(forest, k_, rng, &samples);
+    HypergraphSampler::sample_hypotheses(forest, k_, prng_, &samples);
     for ( size_t i = 0; i < k_; ++i ) {
-      s.sents.push_back( samples[i].words );
-      s.feats.push_back( samples[i].fmap );
-      s.model_scores.push_back( log(samples[i].model_score) );
+      ScoredHyp h;
+      h.w = samples[i].words;
+      h.f = samples[i].fmap;
+      h.model = log(samples[i].model_score); 
+      s_.push_back(h);
     }
   }
 };
diff --git a/dtrain/pairsampling.h b/dtrain/pairsampling.h
index a8521485..2e4ab155 100644
--- a/dtrain/pairsampling.h
+++ b/dtrain/pairsampling.h
@@ -8,47 +8,33 @@ namespace dtrain
 {
 
 
-struct Pair
-{
-  SparseVector<double> first,       second;
-  size_t               first_rank,  second_rank;
-  double               first_score, second_score;
-};
-
 inline void
-sample_all_pairs(Samples* kb, vector<Pair> &training)
+sample_all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> > &training)
 {
-  for (size_t i = 0; i < kb->GetSize()-1; i++) {
-    for (size_t j = i+1; j < kb->GetSize(); j++) {
-      Pair p;
-      p.first = kb->feats[i];
-      p.second = kb->feats[j];
-      p.first_rank = i;
-      p.second_rank = j;
-      p.first_score = kb->scores[i];
-      p.second_score = kb->scores[j];
+  for (size_t i = 0; i < s->size()-1; i++) {
+    for (size_t j = i+1; j < s->size(); j++) {
+      pair<ScoredHyp,ScoredHyp> p;
+      p.first = (*s)[i];
+      p.second = (*s)[j];
       training.push_back(p);
-    } // j
-  } // i
+    }
+  }
 }
 
 inline void
-sample_rand_pairs(Samples* kb, vector<Pair> &training, MT19937* prng)
+sample_rand_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> > &training,
+                  MT19937* prng)
 {
-  for (size_t i = 0; i < kb->GetSize()-1; i++) {
-    for (size_t j = i+1; j < kb->GetSize(); j++) {
+  for (size_t i = 0; i < s->size()-1; i++) {
+    for (size_t j = i+1; j < s->size(); j++) {
       if (prng->next() < .5) {
-        Pair p;
-        p.first = kb->feats[i];
-        p.second = kb->feats[j];
-        p.first_rank = i;
-        p.second_rank = j;
-        p.first_score = kb->scores[i];
-        p.second_score = kb->scores[j];
+        pair<ScoredHyp,ScoredHyp> p;
+        p.first = (*s)[i];
+        p.second = (*s)[j];
         training.push_back(p);
       }
-    } // j
-  } // i
+    }
+  }
 }
 
 
diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini
index aee3c89e..00ba72f9 100644
--- a/dtrain/test/example/dtrain.ini
+++ b/dtrain/test/example/dtrain.ini
@@ -1,11 +1,11 @@
 decoder_config=test/example/cdec.ini
-ksamples=100
-ngrams=3
+k=100
+N=3
 epochs=1000
 input=test/example/nc-1k.gz
 scorer=stupid_bleu
 output=test/example/weights.gz
-stop_after=10
-sample_from=kbest
+stop_after=100
+sample_from=forest
 pair_sampling=all
 print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PassThrough