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#ifndef _DTRAIN_SCORE_H_
#define _DTRAIN_SCORE_H_
#include "dtrain.h"
namespace dtrain
{
struct NgramCounts
{
unsigned N_;
map<unsigned, score_t> clipped_;
map<unsigned, score_t> sum_;
NgramCounts(const unsigned N) : N_(N) { Zero(); }
inline void
operator+=(const NgramCounts& rhs)
{
if (rhs.N_ > N_) Resize(rhs.N_);
for (unsigned i = 0; i < N_; i++) {
this->clipped_[i] += rhs.clipped_.find(i)->second;
this->sum_[i] += rhs.sum_.find(i)->second;
}
}
inline const NgramCounts
operator+(const NgramCounts &other) const
{
NgramCounts result = *this;
result += other;
return result;
}
inline void
operator*=(const score_t rhs)
{
for (unsigned i = 0; i < N_; i++) {
this->clipped_[i] *= rhs;
this->sum_[i] *= rhs;
}
}
inline void
Add(const unsigned count, const unsigned ref_count, const unsigned i)
{
assert(i < N_);
if (count > ref_count) {
clipped_[i] += ref_count;
} else {
clipped_[i] += count;
}
sum_[i] += count;
}
inline void
Zero()
{
for (unsigned i = 0; i < N_; i++) {
clipped_[i] = 0.;
sum_[i] = 0.;
}
}
inline void
One()
{
for (unsigned i = 0; i < N_; i++) {
clipped_[i] = 1.;
sum_[i] = 1.;
}
}
inline void
Print()
{
for (unsigned i = 0; i < N_; i++) {
cout << i+1 << "grams (clipped):\t" << clipped_[i] << endl;
cout << i+1 << "grams:\t\t\t" << sum_[i] << endl;
}
}
inline void Resize(unsigned N)
{
if (N == N_) return;
else if (N > N_) {
for (unsigned i = N_; i < N; i++) {
clipped_[i] = 0.;
sum_[i] = 0.;
}
} else { // N < N_
for (unsigned i = N_-1; i > N-1; i--) {
clipped_.erase(i);
sum_.erase(i);
}
}
N_ = N;
}
};
typedef map<vector<WordID>, unsigned> Ngrams;
inline Ngrams
MakeNgrams(const vector<WordID>& s, const unsigned N)
{
Ngrams ngrams;
vector<WordID> ng;
for (size_t i = 0; i < s.size(); i++) {
ng.clear();
for (unsigned j = i; j < min(i+N, s.size()); j++) {
ng.push_back(s[j]);
ngrams[ng]++;
}
}
return ngrams;
}
inline NgramCounts
MakeNgramCounts(const vector<WordID>& hyp, const vector<vector<WordID> >& refs, const unsigned N)
{
Ngrams hyp_ngrams = MakeNgrams(hyp, N);
vector<Ngrams> refs_ngrams;
for (auto r: refs) {
Ngrams r_ng = MakeNgrams(r, N);
refs_ngrams.push_back(r_ng);
}
NgramCounts counts(N);
Ngrams::iterator it, ti;
for (it = hyp_ngrams.begin(); it != hyp_ngrams.end(); it++) {
unsigned max_ref_count = 0;
for (auto ref_ngrams: refs_ngrams) {
ti = ref_ngrams.find(it->first);
if (ti != ref_ngrams.end())
max_ref_count = max(max_ref_count, ti->second);
}
counts.Add(it->second, min(it->second, max_ref_count), it->first.size() - 1);
}
return counts;
}
/*
* per-sentence BLEU
* as in "Optimizing for Sentence-Level BLEU+1
* Yields Short Translations"
* (Nakov et al. '12)
*
* [simply add 1 to reference length for calculation of BP]
*
*/
struct PerSentenceBleuScorer
{
const unsigned N_;
vector<score_t> w_;
PerSentenceBleuScorer(unsigned n) : N_(n)
{
for (auto i = 1; i <= N_; i++)
w_.push_back(1.0/N_);
}
inline score_t
BrevityPenalty(const unsigned hyp_len, const unsigned ref_len)
{
if (hyp_len > ref_len) return 1;
return exp(1 - (score_t)ref_len/hyp_len);
}
score_t
Score(const vector<WordID>& hyp, const vector<vector<WordID> >& refs)
{
unsigned hyp_len = hyp.size(), ref_len = 0;
// best match reference length
if (refs.size() == 1) {
ref_len = refs[0].size();
} else {
unsigned i = 0, best_idx = 0;
unsigned best = std::numeric_limits<unsigned>::max();
for (auto r: refs) {
unsigned d = abs(hyp_len-r.size());
if (best > d) best_idx = i;
}
ref_len = refs[best_idx].size();
}
if (hyp_len == 0 || ref_len == 0) return 0.;
NgramCounts counts = MakeNgramCounts(hyp, refs, 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 BrevityPenalty(hyp_len, ref_len+1) * exp(sum);
}
};
} // namespace
#endif
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