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// $Id$
// vim:tabstop=2
#pragma once
#include "StaticData.h" // needed for factor splitter
inline bool existsFile(const char* filePath) {
struct stat mystat;
return (stat(filePath,&mystat)==0);
}
double addLogScale(double x,double y)
{
if(x>y) return addLogScale(y,x); else return x+log(1.0+exp(y-x));
}
double Exp(double x)
{
return exp(x);
}
class PDTAimp
{
// only these classes are allowed to instantiate this class
friend class PhraseDictionaryTreeAdaptor;
protected:
PDTAimp(PhraseDictionaryTreeAdaptor *p,unsigned nis)
: m_languageModels(0),m_weightWP(0.0),m_dict(0),
m_obj(p),useCache(1),m_numInputScores(nis),totalE(0),distinctE(0) {}
public:
std::vector<float> m_weights;
LMList const* m_languageModels;
float m_weightWP;
std::vector<FactorType> m_input,m_output;
PhraseDictionaryTree *m_dict;
typedef std::vector<TargetPhraseCollection const*> vTPC;
mutable vTPC m_tgtColls;
typedef std::map<Phrase,TargetPhraseCollection const*> MapSrc2Tgt;
mutable MapSrc2Tgt m_cache;
PhraseDictionaryTreeAdaptor *m_obj;
int useCache;
std::vector<vTPC> m_rangeCache;
unsigned m_numInputScores;
UniqueObjectManager<Phrase> uniqSrcPhr;
size_t totalE,distinctE;
std::vector<size_t> path1Best,pathExplored;
std::vector<double> pathCN;
~PDTAimp()
{
CleanUp();
delete m_dict;
if (StaticData::Instance().GetVerboseLevel() >= 2)
{
TRACE_ERR("tgt candidates stats: total="<<totalE<<"; distinct="
<<distinctE<<" ("<<distinctE/(0.01*totalE)<<"); duplicates="
<<totalE-distinctE<<" ("<<(totalE-distinctE)/(0.01*totalE)
<<")\n");
TRACE_ERR("\npath statistics\n");
if(path1Best.size())
{
TRACE_ERR("1-best: ");
std::copy(path1Best.begin()+1,path1Best.end(),
std::ostream_iterator<size_t>(std::cerr," \t"));
TRACE_ERR("\n");
}
if(pathCN.size())
{
TRACE_ERR("CN (full): ");
std::transform(pathCN.begin()+1
,pathCN.end()
,std::ostream_iterator<double>(std::cerr," \t")
,Exp);
TRACE_ERR("\n");
}
if(pathExplored.size())
{
TRACE_ERR("CN (explored): ");
std::copy(pathExplored.begin()+1,pathExplored.end(),
std::ostream_iterator<size_t>(std::cerr," \t"));
TRACE_ERR("\n");
}
}
}
void Factors2String(Word const& w,std::string& s) const
{
s=w.GetString(m_input,false);
}
void CleanUp()
{
assert(m_dict);
m_dict->FreeMemory();
for(size_t i=0;i<m_tgtColls.size();++i) delete m_tgtColls[i];
m_tgtColls.clear();
m_cache.clear();
m_rangeCache.clear();
uniqSrcPhr.clear();
}
void AddEquivPhrase(const Phrase &source, const TargetPhrase &targetPhrase)
{
assert(GetTargetPhraseCollection(source)==0);
VERBOSE(2, "adding unk source phrase "<<source<<"\n");
std::pair<MapSrc2Tgt::iterator,bool> p
=m_cache.insert(std::make_pair(source,static_cast<TargetPhraseCollection const*>(0)));
if(p.second || p.first->second==0)
{
TargetPhraseCollection *ptr=new TargetPhraseCollection;
ptr->Add(new TargetPhrase(targetPhrase));
p.first->second=ptr;
m_tgtColls.push_back(ptr);
}
else VERBOSE(2, "WARNING: you added an already existing phrase!\n");
}
TargetPhraseCollection const*
GetTargetPhraseCollection(Phrase const &src) const
{
assert(m_dict);
if(src.GetSize()==0) return 0;
std::pair<MapSrc2Tgt::iterator,bool> piter;
if(useCache)
{
piter=m_cache.insert(std::make_pair(src,static_cast<TargetPhraseCollection const*>(0)));
if(!piter.second) return piter.first->second;
}
else if (m_cache.size())
{
MapSrc2Tgt::const_iterator i=m_cache.find(src);
return (i!=m_cache.end() ? i->second : 0);
}
std::vector<std::string> srcString(src.GetSize());
// convert source Phrase into vector of strings
for(size_t i=0;i<srcString.size();++i)
{
Factors2String(src.GetWord(i),srcString[i]);
}
// get target phrases in string representation
std::vector<StringTgtCand> cands;
m_dict->GetTargetCandidates(srcString,cands);
if(cands.empty())
{
return 0;
}
std::vector<TargetPhrase> tCands;tCands.reserve(cands.size());
std::vector<std::pair<float,size_t> > costs;costs.reserve(cands.size());
// convert into TargetPhrases
for(size_t i=0;i<cands.size();++i)
{
TargetPhrase targetPhrase(Output);
StringTgtCand::first_type const& factorStrings=cands[i].first;
StringTgtCand::second_type const& probVector=cands[i].second;
std::vector<float> scoreVector(probVector.size());
std::transform(probVector.begin(),probVector.end(),scoreVector.begin(),
TransformScore);
std::transform(scoreVector.begin(),scoreVector.end(),scoreVector.begin(),
FloorScore);
CreateTargetPhrase(targetPhrase,factorStrings,scoreVector);
costs.push_back(std::make_pair(-targetPhrase.GetFutureScore(),
tCands.size()));
tCands.push_back(targetPhrase);
}
TargetPhraseCollection *rv=PruneTargetCandidates(tCands,costs);
if(rv->IsEmpty())
{
delete rv;
return 0;
}
else
{
if(useCache) piter.first->second=rv;
m_tgtColls.push_back(rv);
return rv;
}
}
void Create(const std::vector<FactorType> &input
, const std::vector<FactorType> &output
, const std::string &filePath
, const std::vector<float> &weight
, const LMList &languageModels
, float weightWP
)
{
// set my members
m_dict=new PhraseDictionaryTree(weight.size()-m_numInputScores);
m_input=input;
m_output=output;
m_languageModels=&languageModels;
m_weightWP=weightWP;
m_weights=weight;
std::string binFname=filePath+".binphr.idx";
if(!existsFile(binFname.c_str())) {
TRACE_ERR( "bin ttable does not exist -> create it\n");
InputFileStream in(filePath);
m_dict->Create(in,filePath);
}
TRACE_ERR( "reading bin ttable\n");
m_dict->Read(filePath);
}
typedef PhraseDictionaryTree::PrefixPtr PPtr;
typedef unsigned short Position;
typedef std::pair<Position,Position> Range;
struct State {
PPtr ptr;
Range range;
float score;
Position realWords;
Phrase src;
State() : range(0,0),score(0.0),realWords(0),src(Input) {}
State(Position b,Position e,const PPtr& v,float sc=0.0,Position rw=0)
: ptr(v),range(b,e),score(sc),realWords(rw),src(Input) {}
State(Range const& r,const PPtr& v,float sc=0.0,Position rw=0)
: ptr(v),range(r),score(sc),realWords(rw),src(Input) {}
Position begin() const {return range.first;}
Position end() const {return range.second;}
float GetScore() const {return score;}
friend std::ostream& operator<<(std::ostream& out,State const& s) {
out<<" R=("<<s.begin()<<","<<s.end()<<"),SC=("<<s.GetScore()<<","<<s.realWords<<")";
return out;
}
};
void CreateTargetPhrase(TargetPhrase& targetPhrase,
StringTgtCand::first_type const& factorStrings,
StringTgtCand::second_type const& scoreVector,
Phrase const* srcPtr=0) const
{
FactorCollection &factorCollection = FactorCollection::Instance();
for(size_t k=0;k<factorStrings.size();++k)
{
std::vector<std::string> factors=TokenizeMultiCharSeparator(*factorStrings[k],StaticData::Instance().GetFactorDelimiter());
Word& w=targetPhrase.AddWord();
for(size_t l=0;l<m_output.size();++l)
w[m_output[l]]= factorCollection.AddFactor(Output, m_output[l], factors[l]);
}
targetPhrase.SetScore(m_obj, scoreVector, m_weights, m_weightWP, *m_languageModels);
targetPhrase.SetSourcePhrase(srcPtr);
}
TargetPhraseCollection* PruneTargetCandidates(std::vector<TargetPhrase> const & tCands,
std::vector<std::pair<float,size_t> >& costs) const
{
// convert into TargetPhraseCollection
TargetPhraseCollection *rv=new TargetPhraseCollection;
// set limit to tableLimit or actual size, whatever is smaller
std::vector<std::pair<float,size_t> >::iterator nth =
costs.begin() + ((m_obj->m_tableLimit>0 && // 0 indicates no limit
m_obj->m_tableLimit < costs.size()) ?
m_obj->m_tableLimit : costs.size());
// find the nth phrase according to future cost
std::nth_element(costs.begin(),nth ,costs.end());
// add n top phrases to the return list
for(std::vector<std::pair<float,size_t> >::iterator
it = costs.begin(); it != nth; ++it)
rv->Add(new TargetPhrase(tCands[it->second]));
return rv;
}
// POD for target phrase scores
struct TScores {
float total;
StringTgtCand::second_type trans;
Phrase const* src;
TScores() : total(0.0),src(0) {}
};
void CacheSource(ConfusionNet const& src)
{
assert(m_dict);
const size_t srcSize=src.GetSize();
std::vector<size_t> exploredPaths(srcSize+1,0);
std::vector<double> exPathsD(srcSize+1,-1.0);
// collect some statistics
std::vector<size_t> cnDepths(srcSize,0);
for(size_t i=0;i<srcSize;++i) cnDepths[i]=src[i].size();
for(size_t len=1;len<=srcSize;++len)
for(size_t i=0;i<=srcSize-len;++i)
{
double pd=0.0; for(size_t k=i;k<i+len;++k) pd+=log(1.0*cnDepths[k]);
exPathsD[len]=(exPathsD[len]>=0.0 ? addLogScale(pd,exPathsD[len]) : pd);
}
// update global statistics
if(pathCN.size()<=srcSize) pathCN.resize(srcSize+1,-1.0);
for(size_t len=1;len<=srcSize;++len)
pathCN[len]=pathCN[len]>=0.0 ? addLogScale(pathCN[len],exPathsD[len]) : exPathsD[len];
if(path1Best.size()<=srcSize) path1Best.resize(srcSize+1,0);
for(size_t len=1;len<=srcSize;++len) path1Best[len]+=srcSize-len+1;
if (StaticData::Instance().GetVerboseLevel() >= 2 && exPathsD.size())
{
TRACE_ERR("path stats for current CN: \nCN (full): ");
std::transform(exPathsD.begin()+1
,exPathsD.end()
,std::ostream_iterator<double>(std::cerr," ")
,Exp);
TRACE_ERR("\n");
}
typedef StringTgtCand::first_type sPhrase;
typedef std::map<StringTgtCand::first_type,TScores> E2Costs;
std::map<Range,E2Costs> cov2cand;
std::vector<State> stack;
for(Position i=0 ; i < srcSize ; ++i)
stack.push_back(State(i, i, m_dict->GetRoot()));
while(!stack.empty())
{
State curr(stack.back());
stack.pop_back();
assert(curr.end()<srcSize);
const ConfusionNet::Column &currCol=src[curr.end()];
// in a given column, loop over all possibilities
for(size_t colidx=0;colidx<currCol.size();++colidx)
{
const Word& w=currCol[colidx].first; // w=the i^th possibility in column colidx
std::string s;
Factors2String(w,s);
bool isEpsilon=(s=="" || s==EPSILON);
// do not start with epsilon (except at first position)
if(isEpsilon && curr.begin()==curr.end() && curr.begin()>0) continue;
// At a given node in the prefix tree, look to see if w defines an edge to
// another node (Extend). Stay at the same node if w==EPSILON
PPtr nextP = (isEpsilon ? curr.ptr : m_dict->Extend(curr.ptr,s));
unsigned newRealWords=curr.realWords + (isEpsilon ? 0 : 1);
if(nextP) // w is a word that should be considered
{
Range newRange(curr.begin(),curr.end()+src.GetColumnIncrement(curr.end(),colidx));
float newScore=curr.GetScore()+currCol[colidx].second; // CN score
Phrase newSrc(curr.src);
if(!isEpsilon) newSrc.AddWord(w);
if(newRange.second<srcSize && newScore>LOWEST_SCORE)
{
// if there is more room to grow, add a new state onto the queue
// to be explored that represents [begin, curEnd+)
stack.push_back(State(newRange,nextP,newScore,newRealWords));
stack.back().src=newSrc;
}
std::vector<StringTgtCand> tcands;
// now, look up the target candidates (aprx. TargetPhraseCollection) for
// the current path through the CN
m_dict->GetTargetCandidates(nextP,tcands);
if(newRange.second>=exploredPaths.size()+newRange.first)
exploredPaths.resize(newRange.second-newRange.first+1,0);
++exploredPaths[newRange.second-newRange.first];
totalE+=tcands.size();
if(tcands.size())
{
E2Costs& e2costs=cov2cand[newRange];
Phrase const* srcPtr=uniqSrcPhr(newSrc);
for(size_t i=0;i<tcands.size();++i)
{
std::vector<float> nscores(tcands[i].second.size()+m_numInputScores,0.0);
switch(m_numInputScores)
{
case 2: nscores[1]= -1.0f * newRealWords; // do not use -newRealWords ! -- RZ
case 1: nscores[0]= newScore;
case 0: break;
default:
TRACE_ERR("ERROR: too many model scaling factors for input weights 'weight-i' : "<<m_numInputScores<<"\n");
abort();
}
std::transform(tcands[i].second.begin(),tcands[i].second.end(),nscores.begin() + m_numInputScores,TransformScore);
assert(nscores.size()==m_weights.size());
float score=std::inner_product(nscores.begin(), nscores.end(), m_weights.begin(), 0.0f);
score-=tcands[i].first.size() * m_weightWP;
std::pair<E2Costs::iterator,bool> p=e2costs.insert(std::make_pair(tcands[i].first,TScores()));
if(p.second) ++distinctE;
TScores & scores=p.first->second;
if(p.second || scores.total<score)
{
scores.total=score;
scores.trans=nscores;
scores.src=srcPtr;
}
}
}
}
}
} // end while(!stack.empty())
if (StaticData::Instance().GetVerboseLevel() >= 2 && exploredPaths.size())
{
TRACE_ERR("CN (explored): ");
std::copy(exploredPaths.begin()+1,exploredPaths.end(),
std::ostream_iterator<size_t>(std::cerr," "));
TRACE_ERR("\n");
}
if(pathExplored.size()<exploredPaths.size())
pathExplored.resize(exploredPaths.size(),0);
for(size_t len=1;len<=srcSize;++len)
pathExplored[len]+=exploredPaths[len];
m_rangeCache.resize(src.GetSize(),vTPC(src.GetSize(),0));
for(std::map<Range,E2Costs>::const_iterator i=cov2cand.begin();i!=cov2cand.end();++i)
{
assert(i->first.first<m_rangeCache.size());
assert(i->first.second>0);
assert(static_cast<size_t>(i->first.second-1)<m_rangeCache[i->first.first].size());
assert(m_rangeCache[i->first.first][i->first.second-1]==0);
std::vector<TargetPhrase> tCands;tCands.reserve(i->second.size());
std::vector<std::pair<float,size_t> > costs;costs.reserve(i->second.size());
for(E2Costs::const_iterator j=i->second.begin();j!=i->second.end();++j)
{
TScores const & scores=j->second;
TargetPhrase targetPhrase(Output);
CreateTargetPhrase(targetPhrase,j->first,scores.trans,scores.src);
costs.push_back(std::make_pair(-targetPhrase.GetFutureScore(),tCands.size()));
tCands.push_back(targetPhrase);
//std::cerr << i->first.first << "-" << i->first.second << ": " << targetPhrase << std::endl;
}
TargetPhraseCollection *rv=PruneTargetCandidates(tCands,costs);
if(rv->IsEmpty())
delete rv;
else
{
m_rangeCache[i->first.first][i->first.second-1]=rv;
m_tgtColls.push_back(rv);
}
}
// free memory
m_dict->FreeMemory();
}
size_t GetNumInputScores() const {return m_numInputScores;}
};
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