major restructure of the training code

1 files changed, 149 insertions, 0 deletions

diff --git a/training/dtrain/pairsampling.h b/training/dtrain/pairsampling.h
new file mode 100644
index 00000000..84be1efb
--- /dev/null
+++ b/training/dtrain/pairsampling.h
@@ -0,0 +1,149 @@
+#ifndef _DTRAIN_PAIRSAMPLING_H_
+#define _DTRAIN_PAIRSAMPLING_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)
+{
+  return a.score > b.score;
+}
+
+inline void
+all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, 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 (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
+ *  cmp middle Y to low X
+ */
+
+inline void
+partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, float hi_lo)
+{
+  unsigned sz = s->size();
+  if (sz < 2) return;
+  sort(s->begin(), s->end(), cmp_hyp_by_score_d);
+  unsigned sep = round(sz*hi_lo);
+  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++) {
+#ifdef DTRAIN_FASTER_PERCEPTRON
+      if ((*s)[i].model <= (*s)[j].model) {
+#endif
+      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;
+      }
+#ifdef DTRAIN_FASTER_PERCEPTRON
+      }
+#endif
+    }
+    if (b) break;
+  }
+  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 < sz-sep_lo; i++) {
+    for (unsigned j = sz-sep_lo; j < sz; j++) {
+#ifdef DTRAIN_FASTER_PERCEPTRON
+      if ((*s)[i].model <= (*s)[j].model) {
+#endif
+      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;
+#ifdef DTRAIN_FASTER_PERCEPTRON
+      }
+#endif
+    }
+  }
+}
+
+/*
+ * pair sampling as in
+ * 'Tuning as Ranking' (Hopkins & May, 2011)
+ *     count = 5000
+ * threshold = 5% BLEU (0.05 for param 3)
+ *       cut = top 50
+ */
+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, float _unused=1)
+{
+  unsigned max_count = 5000, 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)) {
+        training.push_back(make_pair((*s)[i], (*s)[j]));
+        if (++count == max_count) {
+          b = true;
+          break;
+        }
+      }
+    }
+    if (b) break;
+  }
+  if (training.size() > 50) {
+    sort(training.begin(), training.end(), _PRO_cmp_pair_by_diff_d);
+    training.erase(training.begin()+50, training.end());
+  }
+  return;
+}
+
+
+} // namespace
+
+#endif
+