1 files changed, 14 insertions, 100 deletions

diff --git a/training/dtrain/pairs.h b/training/dtrain/pairs.h
index fd08be8c..dea0dabc 100644
--- a/training/dtrain/pairs.h
+++ b/training/dtrain/pairs.h
@@ -1,140 +1,54 @@
-#ifndef _DTRAIN_PAIRSAMPLING_H_
-#define _DTRAIN_PAIRSAMPLING_H_
+#ifndef _DTRAIN_PAIRS_H_
+#define _DTRAIN_PAIRS_H_
 
 namespace dtrain
 {
 
-
 bool
-accept_pair(score_t a, score_t b, score_t threshold)
-{
-  if (fabs(a - b) < threshold) return false;
-  return true;
-}
-
-bool
-cmp_hyp_by_score_d(ScoredHyp a, ScoredHyp b)
+CmpHypsByScore(ScoredHyp a, ScoredHyp b)
 {
   return a.score > b.score;
 }
 
-inline void
-all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, bool misranked_only, float _unused=1)
-{
-  sort(s->begin(), s->end(), cmp_hyp_by_score_d);
-  unsigned sz = s->size();
-  bool b = false;
-  unsigned count = 0;
-  for (unsigned i = 0; i < sz-1; i++) {
-    for (unsigned j = i+1; j < sz; j++) {
-      if (misranked_only && !((*s)[i].model <= (*s)[j].model)) continue;
-      if (threshold > 0) {
-        if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      } else {
-        if ((*s)[i].score != (*s)[j].score)
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      }
-      if (++count == max) {
-        b = true;
-        break;
-      }
-    }
-    if (b) break;
-  }
-}
-
 /*
  * multipartite ranking
  *  sort (descending) by bleu
- *  compare top X to middle Y and low X
+ *  compare top X (hi) to middle Y (med) and low X (lo)
  *  cmp middle Y to low X
  */
-
 inline void
-partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, bool misranked_only, float hi_lo)
+MakePairs(vector<ScoredHyp>* s,
+          vector<pair<ScoredHyp,ScoredHyp> >& training,
+          bool misranked_only,
+          float hi_lo)
 {
   unsigned sz = s->size();
   if (sz < 2) return;
-  sort(s->begin(), s->end(), cmp_hyp_by_score_d);
+  sort(s->begin(), s->end(), CmpHypsByScore);
   unsigned sep = round(sz*hi_lo);
+  // hi vs. med vs. low
   unsigned sep_hi = sep;
   if (sz > 4) while (sep_hi < sz && (*s)[sep_hi-1].score == (*s)[sep_hi].score) ++sep_hi;
   else sep_hi = 1;
-  bool b = false;
-  unsigned count = 0;
   for (unsigned i = 0; i < sep_hi; i++) {
     for (unsigned j = sep_hi; j < sz; j++) {
       if (misranked_only && !((*s)[i].model <= (*s)[j].model)) continue;
-      if (threshold > 0) {
-        if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      } else {
-        if ((*s)[i].score != (*s)[j].score)
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      }
-      if (++count == max) {
-        b = true;
-        break;
-      }
+      if ((*s)[i].score != (*s)[j].score)
+        training.push_back(make_pair((*s)[i], (*s)[j]));
     }
-    if (b) break;
   }
+  // med vs. low
   unsigned sep_lo = sz-sep;
   while (sep_lo > 0 && (*s)[sep_lo-1].score == (*s)[sep_lo].score) --sep_lo;
   for (unsigned i = sep_hi; i < sep_lo; i++) {
     for (unsigned j = sep_lo; j < sz; j++) {
       if (misranked_only && !((*s)[i].model <= (*s)[j].model)) continue;
-      if (threshold > 0) {
-        if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      } else {
-        if ((*s)[i].score != (*s)[j].score)
-          training.push_back(make_pair((*s)[i], (*s)[j]));
-      }
-      if (++count == max) return;
-    }
-  }
-}
-
-/*
- * pair sampling as in
- * 'Tuning as Ranking' (Hopkins & May, 2011)
- *     count = max (5000)
- * threshold = 5% BLEU (0.05 for param 3)
- *       cut = top 10%
- */
-bool
-_PRO_cmp_pair_by_diff_d(pair<ScoredHyp,ScoredHyp> a, pair<ScoredHyp,ScoredHyp> b)
-{
-  return (fabs(a.first.score - a.second.score)) > (fabs(b.first.score - b.second.score));
-}
-inline void
-PROsampling(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, bool _unused=false, float _also_unused=0)
-{
-  sort(s->begin(), s->end(), cmp_hyp_by_score_d);
-  unsigned max_count = max, count = 0, sz = s->size();
-  bool b = false;
-  for (unsigned i = 0; i < sz-1; i++) {
-    for (unsigned j = i+1; j < sz; j++) {
-      if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) {
+      if ((*s)[i].score != (*s)[j].score)
         training.push_back(make_pair((*s)[i], (*s)[j]));
-        if (++count == max_count) {
-          b = true;
-          break;
-        }
-      }
     }
-    if (b) break;
-  }
-  if (training.size() > max/10) {
-    sort(training.begin(), training.end(), _PRO_cmp_pair_by_diff_d);
-    training.erase(training.begin()+(max/10), training.end());
   }
-  return;
 }
 
-
 } // namespace
 
 #endif