major restructure of the training code

1 files changed, 0 insertions, 254 deletions

diff --git a/dtrain/score.cc b/dtrain/score.cc
deleted file mode 100644
index 34fc86a9..00000000
--- a/dtrain/score.cc
+++ /dev/null
@@ -1,254 +0,0 @@
-#include "score.h"
-
-namespace dtrain
-{
-
-
-/*
- * bleu
- *
- * as in "BLEU: a Method for Automatic Evaluation
- *        of Machine Translation"
- * (Papineni et al. '02)
- *
- * NOTE: 0 if for one n \in {1..N} count is 0
- */
-score_t
-BleuScorer::Bleu(NgramCounts& counts, const unsigned hyp_len, const unsigned ref_len)
-{
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  unsigned M = N_;
-  vector<score_t> v = w_;
-  if (ref_len < N_) {
-    M = ref_len;
-    for (unsigned i = 0; i < M; i++) v[i] = 1/((score_t)M);
-  }
-  score_t sum = 0;
-  for (unsigned i = 0; i < M; i++) {
-    if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) return 0.;
-    sum += v[i] * log((score_t)counts.clipped_[i]/counts.sum_[i]);
-  }
-  return brevity_penalty(hyp_len, ref_len) * exp(sum);
-}
-
-score_t
-BleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
-                  const unsigned /*rank*/, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
-  return Bleu(counts, hyp_len, ref_len);
-}
-
-/*
- * 'stupid' bleu
- *
- * as in "ORANGE: a Method for Evaluating
- *        Automatic Evaluation Metrics
- *        for Machine Translation"
- * (Lin & Och '04)
- *
- * NOTE: 0 iff no 1gram match
- */
-score_t
-StupidBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
-                        const unsigned /*rank*/, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
-  unsigned M = N_;
-  vector<score_t> v = w_;
-  if (ref_len < N_) {
-    M = ref_len;
-    for (unsigned i = 0; i < M; i++) v[i] = 1/((score_t)M);
-  }
-  score_t sum = 0, add = 0;
-  for (unsigned i = 0; i < M; i++) {
-    if (i == 0 && (counts.sum_[i] == 0 || counts.clipped_[i] == 0)) return 0.;
-    if (i == 1) add = 1;
-    sum += v[i] * log(((score_t)counts.clipped_[i] + add)/((counts.sum_[i] + add)));
-  }
-  return  brevity_penalty(hyp_len, ref_len) * exp(sum);
-}
-
-/*
- * smooth bleu
- *
- * as in "An End-to-End Discriminative Approach
- *        to Machine Translation"
- * (Liang et al. '06)
- *
- * NOTE: max is 0.9375 (with N=4)
- */
-score_t
-SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
-                        const unsigned /*rank*/, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
-  unsigned M = N_;
-  if (ref_len < N_) M = ref_len;
-  score_t sum = 0.;
-  vector<score_t> i_bleu;
-  for (unsigned i = 0; i < M; i++) i_bleu.push_back(0.);
-  for (unsigned i = 0; i < M; i++) {
-    if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) {
-      break;
-    } else {
-      score_t i_ng = log((score_t)counts.clipped_[i]/counts.sum_[i]);
-      for (unsigned j = i; j < M; j++) {
-        i_bleu[j] += (1/((score_t)j+1)) * i_ng;
-      }
-    }
-    sum += exp(i_bleu[i])/pow(2.0, (double)(N_-i));
-  }
-  return brevity_penalty(hyp_len, ref_len) * sum;
-}
-
-/*
- * 'sum' bleu
- *
- * sum up Ngram precisions
- */
-score_t
-SumBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
-                        const unsigned /*rank*/, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
-  unsigned M = N_;
-  if (ref_len < N_) M = ref_len;
-  score_t sum = 0.;
-  unsigned j = 1;
-  for (unsigned i = 0; i < M; i++) {
-    if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) break;
-    sum += ((score_t)counts.clipped_[i]/counts.sum_[i])/pow(2.0, (double) (N_-j+1));
-    j++;
-  }
-  return brevity_penalty(hyp_len, ref_len) * sum;
-}
-
-/*
- * 'sum' (exp) bleu
- *
- * sum up exp(Ngram precisions)
- */
-score_t
-SumExpBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
-                        const unsigned /*rank*/, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
-  unsigned M = N_;
-  if (ref_len < N_) M = ref_len;
-  score_t sum = 0.;
-  unsigned j = 1;
-  for (unsigned i = 0; i < M; i++) {
-    if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) break;
-    sum += exp(((score_t)counts.clipped_[i]/counts.sum_[i]))/pow(2.0, (double) (N_-j+1));
-    j++;
-  }
-  return brevity_penalty(hyp_len, ref_len) * sum;
-}
-
-/*
- * 'sum' (whatever) bleu
- *
- * sum up exp(weight * log(Ngram precisions))
- */
-score_t
-SumWhateverBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
-                        const unsigned /*rank*/, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (hyp_len == 0 || ref_len == 0) return 0.;
-  NgramCounts counts = make_ngram_counts(hyp, ref, N_);
-  unsigned M = N_;
-  vector<score_t> v = w_;
-  if (ref_len < N_) {
-    M = ref_len;
-    for (unsigned i = 0; i < M; i++) v[i] = 1/((score_t)M);
-  }
-  score_t sum = 0.;
-  unsigned j = 1;
-  for (unsigned i = 0; i < M; i++) {
-    if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) break;
-    sum += exp(v[i] * log(((score_t)counts.clipped_[i]/counts.sum_[i])))/pow(2.0, (double) (N_-j+1));
-    j++;
-  }
-  return brevity_penalty(hyp_len, ref_len) * sum;
-}
-
-/*
- * approx. bleu
- *
- * as in "Online Large-Margin Training of Syntactic
- *        and Structural Translation Features"
- * (Chiang et al. '08)
- *
- * NOTE: Needs some more code in dtrain.cc .
- *       No scaling by src len.
- */
-score_t
-ApproxBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
-                        const unsigned rank, const unsigned src_len)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (ref_len == 0) return 0.;
-  score_t score = 0.;
-  NgramCounts counts(N_);
-  if (hyp_len > 0) {
-    counts = make_ngram_counts(hyp, ref, N_);
-    NgramCounts tmp = glob_onebest_counts_ + counts;
-    score = Bleu(tmp, hyp_len, ref_len);
-  }
-  if (rank == 0) { // 'context of 1best translations'
-    glob_onebest_counts_ += counts;
-    glob_onebest_counts_ *= discount_;
-    glob_hyp_len_ = discount_ * (glob_hyp_len_ + hyp_len);
-    glob_ref_len_ = discount_ * (glob_ref_len_ + ref_len);
-    glob_src_len_ = discount_ * (glob_src_len_ + src_len);
-  }
-  return score;
-}
-
-/*
- * Linear (Corpus) Bleu
- *
- * as in "Lattice Minimum Bayes-Risk Decoding
- *        for Statistical Machine Translation"
- * (Tromble et al. '08)
- *
- */
-score_t
-LinearBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
-                        const unsigned rank, const unsigned /*src_len*/)
-{
-  unsigned hyp_len = hyp.size(), ref_len = ref.size();
-  if (ref_len == 0) return 0.;
-  unsigned M = N_;
-  if (ref_len < N_) M = ref_len;
-  NgramCounts counts(M);
-  if (hyp_len > 0)
-    counts = make_ngram_counts(hyp, ref, M);
-  score_t ret = 0.;
-  for (unsigned i = 0; i < M; i++) {
-    if (counts.sum_[i] == 0 || onebest_counts_.sum_[i] == 0) break;
-    ret += counts.sum_[i]/onebest_counts_.sum_[i];
-  }
-  ret = -(hyp_len/(score_t)onebest_len_) + (1./M) * ret;
-  if (rank == 0) {
-    onebest_len_ += hyp_len;
-    onebest_counts_ += counts;
-  }
-  return ret;
-}
-
-
-} // namespace
-