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-rw-r--r--training/dtrain/pairsampling.h149
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diff --git a/training/dtrain/pairsampling.h b/training/dtrain/pairsampling.h
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+#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
+