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#include <iostream>
#include <fstream>
#include <sstream>
#include <iomanip>
#include <vector>
#include <map>
#include <cstdlib>
#include <cmath>
#include <algorithm>
#include <cstdio>
#include "moses/TrellisPathList.h"
#include "moses/TrellisPath.h"
// #include "moses/StaticData.h"
#include "moses/Util.h"
#include "mbr.h"
using namespace std ;
using namespace Moses;
/* Input :
1. a sorted n-best list, with duplicates filtered out in the following format
0 ||| amr moussa is currently on a visit to libya , tomorrow , sunday , to hold talks with regard to the in sudan . ||| 0 -4.94418 0 0 -2.16036 0 0 -81.4462 -106.593 -114.43 -105.55 -12.7873 -26.9057 -25.3715 -52.9336 7.99917 -24 ||| -4.58432
2. a weight vector
3. bleu order ( default = 4)
4. scaling factor to weigh the weight vector (default = 1.0)
Output :
translations that minimise the Bayes Risk of the n-best list
*/
int BLEU_ORDER = 4;
int SMOOTH = 1;
float min_interval = 1e-4;
void extract_ngrams(const vector<const Factor* >& sentence, map < vector < const Factor* >, int > & allngrams)
{
vector< const Factor* > ngram;
for (int k = 0; k < BLEU_ORDER; k++) {
for(int i =0; i < max((int)sentence.size()-k,0); i++) {
for ( int j = i; j<= i+k; j++) {
ngram.push_back(sentence[j]);
}
++allngrams[ngram];
ngram.clear();
}
}
}
float calculate_score(const vector< vector<const Factor*> > & sents, int ref, int hyp, vector < map < vector < const Factor *>, int > > & ngram_stats )
{
int comps_n = 2*BLEU_ORDER+1;
vector<int> comps(comps_n);
float logbleu = 0.0, brevity;
int hyp_length = sents[hyp].size();
for (int i =0; i<BLEU_ORDER; i++) {
comps[2*i] = 0;
comps[2*i+1] = max(hyp_length-i,0);
}
map< vector < const Factor * > ,int > & hyp_ngrams = ngram_stats[hyp] ;
map< vector < const Factor * >, int > & ref_ngrams = ngram_stats[ref] ;
for (map< vector< const Factor * >, int >::iterator it = hyp_ngrams.begin();
it != hyp_ngrams.end(); it++) {
map< vector< const Factor * >, int >::iterator ref_it = ref_ngrams.find(it->first);
if(ref_it != ref_ngrams.end()) {
comps[2* (it->first.size()-1)] += min(ref_it->second,it->second);
}
}
comps[comps_n-1] = sents[ref].size();
for (int i=0; i<BLEU_ORDER; i++) {
if (comps[0] == 0)
return 0.0;
if ( i > 0 )
logbleu += log((float)comps[2*i]+SMOOTH)-log((float)comps[2*i+1]+SMOOTH);
else
logbleu += log((float)comps[2*i])-log((float)comps[2*i+1]);
}
logbleu /= BLEU_ORDER;
brevity = 1.0-(float)comps[comps_n-1]/comps[1]; // comps[comps_n-1] is the ref length, comps[1] is the test length
if (brevity < 0.0)
logbleu += brevity;
return exp(logbleu);
}
const TrellisPath doMBR(const TrellisPathList& nBestList, AllOptions const& opts)
{
float marginal = 0;
float mbr_scale = opts.mbr.scale;
vector<float> joint_prob_vec;
vector< vector<const Factor*> > translations;
float joint_prob;
vector< map < vector <const Factor *>, int > > ngram_stats;
TrellisPathList::const_iterator iter;
// get max score to prevent underflow
float maxScore = -1e20;
for (iter = nBestList.begin() ; iter != nBestList.end() ; ++iter) {
const TrellisPath &path = **iter;
float score = mbr_scale * path.GetScoreBreakdown()->GetWeightedScore();
if (maxScore < score) maxScore = score;
}
vector<FactorType> const& oFactors = opts.output.factor_order;
UTIL_THROW_IF2(oFactors.size() != 1, "Need exactly one output factor!");
for (iter = nBestList.begin() ; iter != nBestList.end() ; ++iter) {
const TrellisPath &path = **iter;
joint_prob = UntransformScore(mbr_scale * path.GetScoreBreakdown()->GetWeightedScore() - maxScore);
marginal += joint_prob;
joint_prob_vec.push_back(joint_prob);
// get words in translation
vector<const Factor*> translation;
GetOutputFactors(path, oFactors[0], translation);
// collect n-gram counts
map < vector < const Factor *>, int > counts;
extract_ngrams(translation,counts);
ngram_stats.push_back(counts);
translations.push_back(translation);
}
vector<float> mbr_loss;
float bleu, weightedLoss;
float weightedLossCumul = 0;
float minMBRLoss = 1000000;
int minMBRLossIdx = -1;
/* Main MBR computation done here */
iter = nBestList.begin();
for (unsigned int i = 0; i < nBestList.GetSize(); i++) {
weightedLossCumul = 0;
for (unsigned int j = 0; j < nBestList.GetSize(); j++) {
if ( i != j) {
bleu = calculate_score(translations, j, i,ngram_stats );
weightedLoss = ( 1 - bleu) * ( joint_prob_vec[j]/marginal);
weightedLossCumul += weightedLoss;
if (weightedLossCumul > minMBRLoss)
break;
}
}
if (weightedLossCumul < minMBRLoss) {
minMBRLoss = weightedLossCumul;
minMBRLossIdx = i;
}
iter++;
}
/* Find sentence that minimises Bayes Risk under 1- BLEU loss */
return nBestList.at(minMBRLossIdx);
//return translations[minMBRLossIdx];
}
void
GetOutputFactors(const TrellisPath &path, FactorType const oFactor,
vector <const Factor*> &translation)
{
const std::vector<const Hypothesis *> &edges = path.GetEdges();
// print the surface factor of the translation
for (int currEdge = (int)edges.size() - 1 ; currEdge >= 0 ; currEdge--) {
const Hypothesis &edge = *edges[currEdge];
const Phrase &phrase = edge.GetCurrTargetPhrase();
size_t size = phrase.GetSize();
for (size_t pos = 0 ; pos < size ; pos++) {
const Factor *factor = phrase.GetFactor(pos, oFactor);
translation.push_back(factor);
}
}
}
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