moses-smt/mosesdecoder
1
1
Fork 0
mirror of https://github.com/moses-smt/mosesdecoder.git synced 2024-12-28 22:45:50 +03:00
Code Issues Packages Projects Releases Wiki Activity
ce516b770c
mosesdecoder/moses/Manager.cpp
Hieu Hoang 981351758a delete class TranslationSystem
2013-05-11 14:13:26 +01:00

1438 lines
55 KiB
C++
Raw Blame History

// $Id$
// vim:tabstop=2
/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2006 University of Edinburgh
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
***********************************************************************/
#ifdef WIN32
#include <hash_set>
#else
#include <ext/hash_set>
#endif
#include <algorithm>
#include <cmath>
#include <limits>
#include <map>
#include <set>
#include "Manager.h"
#include "TypeDef.h"
#include "Util.h"
#include "TargetPhrase.h"
#include "TrellisPath.h"
#include "TrellisPathCollection.h"
#include "TranslationOption.h"
#include "LexicalReordering.h"
#include "LMList.h"
#include "TranslationOptionCollection.h"
#include "DummyScoreProducers.h"
#include "Timer.h"
#ifdef HAVE_PROTOBUF
#include "hypergraph.pb.h"
#include "rule.pb.h"
#endif
#include "util/exception.hh"
using namespace std;
namespace Moses
{
Manager::Manager(size_t lineNumber, InputType const& source, SearchAlgorithm searchAlgorithm)
:m_transOptColl(source.CreateTranslationOptionCollection())
,m_search(Search::CreateSearch(*this, source, searchAlgorithm, *m_transOptColl))
,interrupted_flag(0)
,m_hypoId(0)
,m_lineNumber(lineNumber)
,m_source(source)
{
StaticData::Instance().InitializeForInput(source);
}
Manager::~Manager()
{
delete m_transOptColl;
delete m_search;
StaticData::Instance().CleanUpAfterSentenceProcessing(m_source);
}
/**
* Main decoder loop that translates a sentence by expanding
* hypotheses stack by stack, until the end of the sentence.
*/
void Manager::ProcessSentence()
{
// reset statistics
ResetSentenceStats(m_source);
Timer getOptionsTime;
getOptionsTime.start();
m_transOptColl->CreateTranslationOptions();
VERBOSE(1, "Line "<< m_lineNumber << ": Collecting options took " << getOptionsTime << " seconds" << endl);
// some reporting on how long this took
IFVERBOSE(2) {
// TODO: XXX: Hack: SentenceStats.h currently requires all values to be of type clock_t
GetSentenceStats().AddTimeCollectOpts((clock_t) (getOptionsTime.get_elapsed_time() * CLOCKS_PER_SEC));
}
// search for best translation with the specified algorithm
Timer searchTime;
searchTime.start();
m_search->ProcessSentence();
VERBOSE(1, "Line " << m_lineNumber << ": Search took " << searchTime << " seconds" << endl);
}
/**
* Print all derivations in search graph. Note: The number of derivations is exponential in the sentence length
*
*/
void Manager::PrintAllDerivations(long translationId, ostream& outputStream) const
{
const std::vector < HypothesisStack* > &hypoStackColl = m_search->GetHypothesisStacks();
vector<const Hypothesis*> sortedPureHypo = hypoStackColl.back()->GetSortedList();
if (sortedPureHypo.size() == 0)
return;
float remainingScore = 0;
vector<const TargetPhrase*> remainingPhrases;
// add all pure paths
vector<const Hypothesis*>::const_iterator iterBestHypo;
for (iterBestHypo = sortedPureHypo.begin()
; iterBestHypo != sortedPureHypo.end()
; ++iterBestHypo) {
printThisHypothesis(translationId, *iterBestHypo, remainingPhrases, remainingScore, outputStream);
printDivergentHypothesis(translationId, *iterBestHypo, remainingPhrases, remainingScore, outputStream);
}
}
const TranslationOptionCollection* Manager::getSntTranslationOptions()
{
return m_transOptColl;
}
void Manager::printDivergentHypothesis(long translationId, const Hypothesis* hypo, const vector <const TargetPhrase*> & remainingPhrases, float remainingScore , ostream& outputStream ) const
{
//Backtrack from the predecessor
if (hypo->GetId() > 0) {
vector <const TargetPhrase*> followingPhrases;
followingPhrases.push_back(& (hypo->GetCurrTargetPhrase()));
///((Phrase) hypo->GetPrevHypo()->GetTargetPhrase());
followingPhrases.insert(followingPhrases.end()--, remainingPhrases.begin(), remainingPhrases.end());
printDivergentHypothesis(translationId, hypo->GetPrevHypo(), followingPhrases , remainingScore + hypo->GetScore() - hypo->GetPrevHypo()->GetScore(), outputStream);
}
//Process the arcs
const ArcList *pAL = hypo->GetArcList();
if (pAL) {
const ArcList &arcList = *pAL;
// every possible Arc to replace this edge
ArcList::const_iterator iterArc;
for (iterArc = arcList.begin() ; iterArc != arcList.end() ; ++iterArc) {
const Hypothesis *loserHypo = *iterArc;
const Hypothesis* loserPrevHypo = loserHypo->GetPrevHypo();
float arcScore = loserHypo->GetScore() - loserPrevHypo->GetScore();
vector <const TargetPhrase* > followingPhrases;
followingPhrases.push_back(&(loserHypo->GetCurrTargetPhrase()));
followingPhrases.insert(followingPhrases.end()--, remainingPhrases.begin(), remainingPhrases.end());
printThisHypothesis(translationId, loserPrevHypo, followingPhrases, remainingScore + arcScore, outputStream);
printDivergentHypothesis(translationId, loserPrevHypo, followingPhrases, remainingScore + arcScore, outputStream);
}
}
}
void Manager::printThisHypothesis(long translationId, const Hypothesis* hypo, const vector <const TargetPhrase*> & remainingPhrases, float remainingScore, ostream& outputStream) const
{
outputStream << translationId << " ||| ";
//Yield of this hypothesis
hypo->ToStream(outputStream);
for (size_t p = 0; p < remainingPhrases.size(); ++p) {
const TargetPhrase * phrase = remainingPhrases[p];
size_t size = phrase->GetSize();
for (size_t pos = 0 ; pos < size ; pos++) {
const Factor *factor = phrase->GetFactor(pos, 0);
outputStream << *factor;
outputStream << " ";
}
}
outputStream << "||| " << hypo->GetScore() + remainingScore;
outputStream << endl;
}
/**
* After decoding, the hypotheses in the stacks and additional arcs
* form a search graph that can be mined for n-best lists.
* The heavy lifting is done in the TrellisPath and TrellisPathCollection
* this function controls this for one sentence.
*
* \param count the number of n-best translations to produce
* \param ret holds the n-best list that was calculated
*/
void Manager::CalcNBest(size_t count, TrellisPathList &ret,bool onlyDistinct) const
{
if (count <= 0)
return;
const std::vector < HypothesisStack* > &hypoStackColl = m_search->GetHypothesisStacks();
vector<const Hypothesis*> sortedPureHypo = hypoStackColl.back()->GetSortedList();
if (sortedPureHypo.size() == 0)
return;
TrellisPathCollection contenders;
set<Phrase> distinctHyps;
// add all pure paths
vector<const Hypothesis*>::const_iterator iterBestHypo;
for (iterBestHypo = sortedPureHypo.begin()
; iterBestHypo != sortedPureHypo.end()
; ++iterBestHypo) {
contenders.Add(new TrellisPath(*iterBestHypo));
}
// factor defines stopping point for distinct n-best list if too many candidates identical
size_t nBestFactor = StaticData::Instance().GetNBestFactor();
if (nBestFactor < 1) nBestFactor = 1000; // 0 = unlimited
// MAIN loop
for (size_t iteration = 0 ; (onlyDistinct ? distinctHyps.size() : ret.GetSize()) < count && contenders.GetSize() > 0 && (iteration < count * nBestFactor) ; iteration++) {
// get next best from list of contenders
TrellisPath *path = contenders.pop();
CHECK(path);
// create deviations from current best
path->CreateDeviantPaths(contenders);
if(onlyDistinct) {
Phrase tgtPhrase = path->GetSurfacePhrase();
if (distinctHyps.insert(tgtPhrase).second) {
ret.Add(path);
} else {
delete path;
path = NULL;
}
} else {
ret.Add(path);
}
if(onlyDistinct) {
const size_t nBestFactor = StaticData::Instance().GetNBestFactor();
if (nBestFactor > 0)
contenders.Prune(count * nBestFactor);
} else {
contenders.Prune(count);
}
}
}
struct SGNReverseCompare {
bool operator() (const SearchGraphNode& s1, const SearchGraphNode& s2) const {
return s1.hypo->GetId() > s2.hypo->GetId();
}
};
/**
* Implements lattice sampling, as in Chatterjee & Cancedda, emnlp 2010
**/
void Manager::CalcLatticeSamples(size_t count, TrellisPathList &ret) const {
vector<SearchGraphNode> searchGraph;
GetSearchGraph(searchGraph);
//Calculation of the sigmas of each hypothesis and edge. In C&C notation this is
//the "log of the cumulative unnormalized probability of all the paths in the
// lattice for the hypothesis to a final node"
typedef pair<int, int> Edge;
map<const Hypothesis*, float> sigmas;
map<Edge, float> edgeScores;
map<const Hypothesis*, set<const Hypothesis*> > outgoingHyps;
map<int,const Hypothesis*> idToHyp;
map<int,float> fscores;
//Iterating through the hypos in reverse order of id gives a reverse
//topological order. We rely on the fact that hypo ids are given out
//sequentially, as the search proceeds.
//NB: Could just sort by stack.
sort(searchGraph.begin(), searchGraph.end(), SGNReverseCompare());
//first task is to fill in the outgoing hypos and edge scores.
for (vector<SearchGraphNode>::const_iterator i = searchGraph.begin();
i != searchGraph.end(); ++i) {
const Hypothesis* hypo = i->hypo;
idToHyp[hypo->GetId()] = hypo;
fscores[hypo->GetId()] = i->fscore;
if (hypo->GetId()) {
//back to current
const Hypothesis* prevHypo = i->hypo->GetPrevHypo();
outgoingHyps[prevHypo].insert(hypo);
edgeScores[Edge(prevHypo->GetId(),hypo->GetId())] =
hypo->GetScore() - prevHypo->GetScore();
}
//forward from current
if (i->forward >= 0) {
map<int,const Hypothesis*>::const_iterator idToHypIter = idToHyp.find(i->forward);
CHECK(idToHypIter != idToHyp.end());
const Hypothesis* nextHypo = idToHypIter->second;
outgoingHyps[hypo].insert(nextHypo);
map<int,float>::const_iterator fscoreIter = fscores.find(nextHypo->GetId());
CHECK(fscoreIter != fscores.end());
edgeScores[Edge(hypo->GetId(),nextHypo->GetId())] =
i->fscore - fscoreIter->second;
}
}
//then run through again to calculate sigmas
for (vector<SearchGraphNode>::const_iterator i = searchGraph.begin();
i != searchGraph.end(); ++i) {
if (i->forward == -1) {
sigmas[i->hypo] = 0;
} else {
map<const Hypothesis*, set<const Hypothesis*> >::const_iterator outIter =
outgoingHyps.find(i->hypo);
CHECK(outIter != outgoingHyps.end());
float sigma = 0;
for (set<const Hypothesis*>::const_iterator j = outIter->second.begin();
j != outIter->second.end(); ++j) {
map<const Hypothesis*, float>::const_iterator succIter = sigmas.find(*j);
CHECK(succIter != sigmas.end());
map<Edge,float>::const_iterator edgeScoreIter =
edgeScores.find(Edge(i->hypo->GetId(),(*j)->GetId()));
CHECK(edgeScoreIter != edgeScores.end());
float term = edgeScoreIter->second + succIter->second; // Add sigma(*j)
if (sigma == 0) {
sigma = term;
} else {
sigma = log_sum(sigma,term);
}
}
sigmas[i->hypo] = sigma;
}
}
//The actual sampling!
const Hypothesis* startHypo = searchGraph.back().hypo;
CHECK(startHypo->GetId() == 0);
for (size_t i = 0; i < count; ++i) {
vector<const Hypothesis*> path;
path.push_back(startHypo);
while(1) {
map<const Hypothesis*, set<const Hypothesis*> >::const_iterator outIter =
outgoingHyps.find(path.back());
if (outIter == outgoingHyps.end() || !outIter->second.size()) {
//end of the path
break;
}
//score the possibles
vector<const Hypothesis*> candidates;
vector<float> candidateScores;
float scoreTotal = 0;
for (set<const Hypothesis*>::const_iterator j = outIter->second.begin();
j != outIter->second.end(); ++j) {
candidates.push_back(*j);
CHECK(sigmas.find(*j) != sigmas.end());
Edge edge(path.back()->GetId(),(*j)->GetId());
CHECK(edgeScores.find(edge) != edgeScores.end());
candidateScores.push_back(sigmas[*j] + edgeScores[edge]);
if (scoreTotal == 0) {
scoreTotal = candidateScores.back();
} else {
scoreTotal = log_sum(candidateScores.back(), scoreTotal);
}
}
//normalise
transform(candidateScores.begin(), candidateScores.end(), candidateScores.begin(), bind2nd(minus<float>(),scoreTotal));
//copy(candidateScores.begin(),candidateScores.end(),ostream_iterator<float>(cerr," "));
//cerr << endl;
//draw the sample
float random = log((float)rand()/RAND_MAX);
size_t position = 1;
float sum = candidateScores[0];
for (; position < candidateScores.size() && sum < random; ++position) {
sum = log_sum(sum,candidateScores[position]);
}
//cerr << "Random: " << random << " Chose " << position-1 << endl;
const Hypothesis* chosen = candidates[position-1];
path.push_back(chosen);
}
//cerr << "Path: " << endl;
//for (size_t j = 0; j < path.size(); ++j) {
// cerr << path[j]->GetId() << " " << path[j]->GetScoreBreakdown() << endl;
//}
//cerr << endl;
//Convert the hypos to TrellisPath
ret.Add(new TrellisPath(path));
//cerr << ret.at(ret.GetSize()-1).GetScoreBreakdown() << endl;
}
}
void Manager::CalcDecoderStatistics() const
{
const Hypothesis *hypo = GetBestHypothesis();
if (hypo != NULL) {
GetSentenceStats().CalcFinalStats(*hypo);
IFVERBOSE(2) {
if (hypo != NULL) {
string buff;
string buff2;
TRACE_ERR( "Source and Target Units:"
<< hypo->GetInput());
buff2.insert(0,"] ");
buff2.insert(0,(hypo->GetCurrTargetPhrase()).ToString());
buff2.insert(0,":");
buff2.insert(0,(hypo->GetCurrSourceWordsRange()).ToString());
buff2.insert(0,"[");
hypo = hypo->GetPrevHypo();
while (hypo != NULL) {
//dont print out the empty final hypo
buff.insert(0,buff2);
buff2.clear();
buff2.insert(0,"] ");
buff2.insert(0,(hypo->GetCurrTargetPhrase()).ToString());
buff2.insert(0,":");
buff2.insert(0,(hypo->GetCurrSourceWordsRange()).ToString());
buff2.insert(0,"[");
hypo = hypo->GetPrevHypo();
}
TRACE_ERR( buff << endl);
}
}
}
}
void OutputWordGraph(std::ostream &outputWordGraphStream, const Hypothesis *hypo, size_t &linkId)
{
const Hypothesis *prevHypo = hypo->GetPrevHypo();
outputWordGraphStream << "J=" << linkId++
<< "\tS=" << prevHypo->GetId()
<< "\tE=" << hypo->GetId()
<< "\ta=";
// phrase table scores
const StaticData &staticData = StaticData::Instance();
const std::vector<PhraseDictionary*> &phraseTables = staticData.GetPhraseDictionaries();
std::vector<PhraseDictionary*>::const_iterator iterPhraseTable;
for (iterPhraseTable = phraseTables.begin() ; iterPhraseTable != phraseTables.end() ; ++iterPhraseTable) {
const PhraseDictionary *phraseTable = *iterPhraseTable;
vector<float> scores = hypo->GetScoreBreakdown().GetScoresForProducer(phraseTable);
outputWordGraphStream << scores[0];
vector<float>::const_iterator iterScore;
for (iterScore = ++scores.begin() ; iterScore != scores.end() ; ++iterScore) {
outputWordGraphStream << ", " << *iterScore;
}
}
// language model scores
outputWordGraphStream << "\tl=";
const LMList &lmList = StaticData::Instance().GetLMList();
LMList::const_iterator iterLM;
for (iterLM = lmList.begin() ; iterLM != lmList.end() ; ++iterLM) {
LanguageModel *lm = *iterLM;
vector<float> scores = hypo->GetScoreBreakdown().GetScoresForProducer(lm);
outputWordGraphStream << scores[0];
vector<float>::const_iterator iterScore;
for (iterScore = ++scores.begin() ; iterScore != scores.end() ; ++iterScore) {
outputWordGraphStream << ", " << *iterScore;
}
}
// re-ordering
outputWordGraphStream << "\tr=";
const std::vector<FeatureFunction*> &ffs = FeatureFunction::GetFeatureFunctions();
std::vector<FeatureFunction*>::const_iterator iter;
for (iter = ffs.begin(); iter != ffs.end(); ++iter) {
const FeatureFunction *ff = *iter;
const DistortionScoreProducer *model = dynamic_cast<const DistortionScoreProducer*>(ff);
if (model) {
outputWordGraphStream << hypo->GetScoreBreakdown().GetScoreForProducer(model);
}
}
// lexicalised re-ordering
/*
const std::vector<LexicalReordering*> &lexOrderings = StaticData::Instance().GetReorderModels();
std::vector<LexicalReordering*>::const_iterator iterLexOrdering;
for (iterLexOrdering = lexOrderings.begin() ; iterLexOrdering != lexOrderings.end() ; ++iterLexOrdering) {
LexicalReordering *lexicalReordering = *iterLexOrdering;
vector<float> scores = hypo->GetScoreBreakdown().GetScoresForProducer(lexicalReordering);
outputWordGraphStream << scores[0];
vector<float>::const_iterator iterScore;
for (iterScore = ++scores.begin() ; iterScore != scores.end() ; ++iterScore) {
outputWordGraphStream << ", " << *iterScore;
}
}
*/
// words !!
// outputWordGraphStream << "\tw=" << hypo->GetCurrTargetPhrase();
// output both source and target phrases in the word graph
outputWordGraphStream << "\tw=" << hypo->GetSourcePhraseStringRep() << "|" << hypo->GetCurrTargetPhrase();
outputWordGraphStream << endl;
}
void Manager::GetWordGraph(long translationId, std::ostream &outputWordGraphStream) const
{
const StaticData &staticData = StaticData::Instance();
string fileName = staticData.GetParam("output-word-graph")[0];
bool outputNBest = Scan<bool>(staticData.GetParam("output-word-graph")[1]);
const std::vector < HypothesisStack* > &hypoStackColl = m_search->GetHypothesisStacks();
outputWordGraphStream << "VERSION=1.0" << endl
<< "UTTERANCE=" << translationId << endl;
size_t linkId = 0;
size_t stackNo = 1;
std::vector < HypothesisStack* >::const_iterator iterStack;
for (iterStack = ++hypoStackColl.begin() ; iterStack != hypoStackColl.end() ; ++iterStack) {
const HypothesisStack &stack = **iterStack;
HypothesisStack::const_iterator iterHypo;
for (iterHypo = stack.begin() ; iterHypo != stack.end() ; ++iterHypo) {
const Hypothesis *hypo = *iterHypo;
OutputWordGraph(outputWordGraphStream, hypo, linkId);
if (outputNBest) {
const ArcList *arcList = hypo->GetArcList();
if (arcList != NULL) {
ArcList::const_iterator iterArcList;
for (iterArcList = arcList->begin() ; iterArcList != arcList->end() ; ++iterArcList) {
const Hypothesis *loserHypo = *iterArcList;
OutputWordGraph(outputWordGraphStream, loserHypo, linkId);
}
}
} //if (outputNBest)
} //for (iterHypo
} // for (iterStack
}
void Manager::GetSearchGraph(vector<SearchGraphNode>& searchGraph) const
{
std::map < int, bool > connected;
std::map < int, int > forward;
std::map < int, double > forwardScore;
// *** find connected hypotheses ***
std::vector< const Hypothesis *> connectedList;
GetConnectedGraph(&connected, &connectedList);
// ** compute best forward path for each hypothesis *** //
// forward cost of hypotheses on final stack is 0
const std::vector < HypothesisStack* > &hypoStackColl = m_search->GetHypothesisStacks();
const HypothesisStack &finalStack = *hypoStackColl.back();
HypothesisStack::const_iterator iterHypo;
for (iterHypo = finalStack.begin() ; iterHypo != finalStack.end() ; ++iterHypo) {
const Hypothesis *hypo = *iterHypo;
forwardScore[ hypo->GetId() ] = 0.0f;
forward[ hypo->GetId() ] = -1;
}
// compete for best forward score of previous hypothesis
std::vector < HypothesisStack* >::const_iterator iterStack;
for (iterStack = --hypoStackColl.end() ; iterStack != hypoStackColl.begin() ; --iterStack) {
const HypothesisStack &stack = **iterStack;
HypothesisStack::const_iterator iterHypo;
for (iterHypo = stack.begin() ; iterHypo != stack.end() ; ++iterHypo) {
const Hypothesis *hypo = *iterHypo;
if (connected.find( hypo->GetId() ) != connected.end()) {
// make a play for previous hypothesis
const Hypothesis *prevHypo = hypo->GetPrevHypo();
double fscore = forwardScore[ hypo->GetId() ] +
hypo->GetScore() - prevHypo->GetScore();
if (forwardScore.find( prevHypo->GetId() ) == forwardScore.end()
|| forwardScore.find( prevHypo->GetId() )->second < fscore) {
forwardScore[ prevHypo->GetId() ] = fscore;
forward[ prevHypo->GetId() ] = hypo->GetId();
}
// all arcs also make a play
const ArcList *arcList = hypo->GetArcList();
if (arcList != NULL) {
ArcList::const_iterator iterArcList;
for (iterArcList = arcList->begin() ; iterArcList != arcList->end() ; ++iterArcList) {
const Hypothesis *loserHypo = *iterArcList;
// make a play
const Hypothesis *loserPrevHypo = loserHypo->GetPrevHypo();
double fscore = forwardScore[ hypo->GetId() ] +
loserHypo->GetScore() - loserPrevHypo->GetScore();
if (forwardScore.find( loserPrevHypo->GetId() ) == forwardScore.end()
|| forwardScore.find( loserPrevHypo->GetId() )->second < fscore) {
forwardScore[ loserPrevHypo->GetId() ] = fscore;
forward[ loserPrevHypo->GetId() ] = loserHypo->GetId();
}
} // end for arc list
} // end if arc list empty
} // end if hypo connected
} // end for hypo
} // end for stack
// *** output all connected hypotheses *** //
connected[ 0 ] = true;
for (iterStack = hypoStackColl.begin() ; iterStack != hypoStackColl.end() ; ++iterStack) {
const HypothesisStack &stack = **iterStack;
HypothesisStack::const_iterator iterHypo;
for (iterHypo = stack.begin() ; iterHypo != stack.end() ; ++iterHypo) {
const Hypothesis *hypo = *iterHypo;
if (connected.find( hypo->GetId() ) != connected.end()) {
searchGraph.push_back(SearchGraphNode(hypo,NULL,forward[hypo->GetId()],
forwardScore[hypo->GetId()]));
const ArcList *arcList = hypo->GetArcList();
if (arcList != NULL) {
ArcList::const_iterator iterArcList;
for (iterArcList = arcList->begin() ; iterArcList != arcList->end() ; ++iterArcList) {
const Hypothesis *loserHypo = *iterArcList;
searchGraph.push_back(SearchGraphNode(loserHypo,hypo,
forward[hypo->GetId()], forwardScore[hypo->GetId()]));
}
} // end if arcList empty
} // end if connected
} // end for iterHypo
} // end for iterStack
}
void Manager::OutputFeatureWeightsForSLF(std::ostream &outputSearchGraphStream) const
{
outputSearchGraphStream.setf(std::ios::fixed);
outputSearchGraphStream.precision(6);
const StaticData& staticData = StaticData::Instance();
const vector<const StatelessFeatureFunction*>& slf = StatelessFeatureFunction::GetStatelessFeatureFunctions();
const vector<const StatefulFeatureFunction*>& sff = StatefulFeatureFunction::GetStatefulFeatureFunctions();
size_t featureIndex = 1;
for (size_t i = 0; i < sff.size(); ++i) {
featureIndex = OutputFeatureWeightsForSLF(featureIndex, sff[i], outputSearchGraphStream);
}
for (size_t i = 0; i < slf.size(); ++i) {
/*
if (slf[i]->GetScoreProducerWeightShortName() != "u" &&
slf[i]->GetScoreProducerWeightShortName() != "tm" &&
slf[i]->GetScoreProducerWeightShortName() != "I" &&
slf[i]->GetScoreProducerWeightShortName() != "g")
*/
{
featureIndex = OutputFeatureWeightsForSLF(featureIndex, slf[i], outputSearchGraphStream);
}
}
const vector<PhraseDictionary*>& pds = staticData.GetPhraseDictionaries();
for( size_t i=0; i<pds.size(); i++ ) {
featureIndex = OutputFeatureWeightsForSLF(featureIndex, pds[i], outputSearchGraphStream);
}
const vector<const GenerationDictionary*>& gds = staticData.GetGenerationDictionaries();
for( size_t i=0; i<gds.size(); i++ ) {
featureIndex = OutputFeatureWeightsForSLF(featureIndex, gds[i], outputSearchGraphStream);
}
}
void Manager::OutputFeatureValuesForSLF(const Hypothesis* hypo, bool zeros, std::ostream &outputSearchGraphStream) const
{
outputSearchGraphStream.setf(std::ios::fixed);
outputSearchGraphStream.precision(6);
// outputSearchGraphStream << endl;
// outputSearchGraphStream << (*hypo) << endl;
// const ScoreComponentCollection& scoreCollection = hypo->GetScoreBreakdown();
// outputSearchGraphStream << scoreCollection << endl;
const StaticData& staticData = StaticData::Instance();
const vector<const StatelessFeatureFunction*>& slf =StatelessFeatureFunction::GetStatelessFeatureFunctions();
const vector<const StatefulFeatureFunction*>& sff = StatefulFeatureFunction::GetStatefulFeatureFunctions();
size_t featureIndex = 1;
for (size_t i = 0; i < sff.size(); ++i) {
featureIndex = OutputFeatureValuesForSLF(featureIndex, zeros, hypo, sff[i], outputSearchGraphStream);
}
for (size_t i = 0; i < slf.size(); ++i) {
/*
if (slf[i]->GetScoreProducerWeightShortName() != "u" &&
slf[i]->GetScoreProducerWeightShortName() != "tm" &&
slf[i]->GetScoreProducerWeightShortName() != "I" &&
slf[i]->GetScoreProducerWeightShortName() != "g")
*/
{
featureIndex = OutputFeatureValuesForSLF(featureIndex, zeros, hypo, slf[i], outputSearchGraphStream);
}
}
const vector<PhraseDictionary*>& pds = staticData.GetPhraseDictionaries();
for( size_t i=0; i<pds.size(); i++ ) {
featureIndex = OutputFeatureValuesForSLF(featureIndex, zeros, hypo, pds[i], outputSearchGraphStream);
}
const vector<const GenerationDictionary*>& gds = staticData.GetGenerationDictionaries();
for( size_t i=0; i<gds.size(); i++ ) {
featureIndex = OutputFeatureValuesForSLF(featureIndex, zeros, hypo, gds[i], outputSearchGraphStream);
}
}
void Manager::OutputFeatureValuesForHypergraph(const Hypothesis* hypo, std::ostream &outputSearchGraphStream) const
{
outputSearchGraphStream.setf(std::ios::fixed);
outputSearchGraphStream.precision(6);
const StaticData& staticData = StaticData::Instance();
const vector<const StatelessFeatureFunction*>& slf =StatelessFeatureFunction::GetStatelessFeatureFunctions();
const vector<const StatefulFeatureFunction*>& sff = StatefulFeatureFunction::GetStatefulFeatureFunctions();
size_t featureIndex = 1;
for (size_t i = 0; i < sff.size(); ++i) {
featureIndex = OutputFeatureValuesForHypergraph(featureIndex, hypo, sff[i], outputSearchGraphStream);
}
for (size_t i = 0; i < slf.size(); ++i) {
/*
if (slf[i]->GetScoreProducerWeightShortName() != "u" &&
slf[i]->GetScoreProducerWeightShortName() != "tm" &&
slf[i]->GetScoreProducerWeightShortName() != "I" &&
slf[i]->GetScoreProducerWeightShortName() != "g")
*/
{
featureIndex = OutputFeatureValuesForHypergraph(featureIndex, hypo, slf[i], outputSearchGraphStream);
}
}
const vector<PhraseDictionary*>& pds = staticData.GetPhraseDictionaries();
for( size_t i=0; i<pds.size(); i++ ) {
featureIndex = OutputFeatureValuesForHypergraph(featureIndex, hypo, pds[i], outputSearchGraphStream);
}
const vector<const GenerationDictionary*>& gds = staticData.GetGenerationDictionaries();
for( size_t i=0; i<gds.size(); i++ ) {
featureIndex = OutputFeatureValuesForHypergraph(featureIndex, hypo, gds[i], outputSearchGraphStream);
}
}
size_t Manager::OutputFeatureWeightsForSLF(size_t index, const FeatureFunction* ff, std::ostream &outputSearchGraphStream) const
{
size_t numScoreComps = ff->GetNumScoreComponents();
if (numScoreComps != 0) {
vector<float> values = StaticData::Instance().GetAllWeights().GetScoresForProducer(ff);
for (size_t i = 0; i < numScoreComps; ++i) {
outputSearchGraphStream << "# " << ff->GetScoreProducerDescription()
<< " " << ff->GetScoreProducerDescription()
<< " " << (i+1) << " of " << numScoreComps << endl
<< "x" << (index+i) << "scale=" << values[i] << endl;
}
return index+numScoreComps;
} else {
cerr << "Sparse features are not supported when outputting HTK standard lattice format" << endl;
assert(false);
return 0;
}
}
size_t Manager::OutputFeatureValuesForSLF(size_t index, bool zeros, const Hypothesis* hypo, const FeatureFunction* ff, std::ostream &outputSearchGraphStream) const
{
// { const FeatureFunction* sp = ff;
// const FVector& m_scores = scoreCollection.GetScoresVector();
// FVector& scores = const_cast<FVector&>(m_scores);
// std::string prefix = sp->GetScoreProducerDescription() + FName::SEP;
// // std::cout << "prefix==" << prefix << endl;
// // cout << "m_scores==" << m_scores << endl;
// // cout << "m_scores.size()==" << m_scores.size() << endl;
// // cout << "m_scores.coreSize()==" << m_scores.coreSize() << endl;
// // cout << "m_scores.cbegin() ?= m_scores.cend()\t" << (m_scores.cbegin() == m_scores.cend()) << endl;
// // for(FVector::FNVmap::const_iterator i = m_scores.cbegin(); i != m_scores.cend(); i++) {
// // std::cout<<prefix << "\t" << (i->first) << "\t" << (i->second) << std::endl;
// // }
// for(int i=0, n=v.size(); i<n; i+=1) {
// // outputSearchGraphStream << prefix << i << "==" << v[i] << std::endl;
// }
// }
// FVector featureValues = scoreCollection.GetVectorForProducer(ff);
// outputSearchGraphStream << featureValues << endl;
const ScoreComponentCollection& scoreCollection = hypo->GetScoreBreakdown();
vector<float> featureValues = scoreCollection.GetScoresForProducer(ff);
size_t numScoreComps = featureValues.size();//featureValues.coreSize();
// if (numScoreComps != ScoreProducer::unlimited) {
// vector<float> values = StaticData::Instance().GetAllWeights().GetScoresForProducer(ff);
for (size_t i = 0; i < numScoreComps; ++i) {
outputSearchGraphStream << "x" << (index+i) << "=" << ((zeros) ? 0.0 : featureValues[i]) << " ";
}
return index+numScoreComps;
// } else {
// cerr << "Sparse features are not supported when outputting HTK standard lattice format" << endl;
// assert(false);
// return 0;
// }
}
size_t Manager::OutputFeatureValuesForHypergraph(size_t index, const Hypothesis* hypo, const FeatureFunction* ff, std::ostream &outputSearchGraphStream) const
{
ScoreComponentCollection scoreCollection = hypo->GetScoreBreakdown();
const Hypothesis *prevHypo = hypo->GetPrevHypo();
if (prevHypo) {
scoreCollection.MinusEquals( prevHypo->GetScoreBreakdown() );
}
vector<float> featureValues = scoreCollection.GetScoresForProducer(ff);
size_t numScoreComps = featureValues.size();
if (numScoreComps > 1) {
for (size_t i = 0; i < numScoreComps; ++i) {
outputSearchGraphStream << ff->GetScoreProducerDescription() << i << "=" << featureValues[i] << " ";
}
} else {
outputSearchGraphStream << ff->GetScoreProducerDescription() << "=" << featureValues[0] << " ";
}
return index+numScoreComps;
}
/**! Output search graph in hypergraph format of Kenneth Heafield's lazy hypergraph decoder */
void Manager::OutputSearchGraphAsHypergraph(long translationId, std::ostream &outputSearchGraphStream) const
{
VERBOSE(2,"Getting search graph to output as hypergraph for sentence " << translationId << std::endl)
vector<SearchGraphNode> searchGraph;
GetSearchGraph(searchGraph);
map<int,int> mosesIDToHypergraphID;
// map<int,int> hypergraphIDToMosesID;
set<int> terminalNodes;
multimap<int,int> hypergraphIDToArcs;
VERBOSE(2,"Gathering information about search graph to output as hypergraph for sentence " << translationId << std::endl)
long numNodes = 0;
long endNode = 0;
{
long hypergraphHypothesisID = 0;
for (size_t arcNumber = 0, size=searchGraph.size(); arcNumber < size; ++arcNumber) {
// Get an id number for the previous hypothesis
const Hypothesis *prevHypo = searchGraph[arcNumber].hypo->GetPrevHypo();
if (prevHypo!=NULL) {
int mosesPrevHypothesisID = prevHypo->GetId();
if (mosesIDToHypergraphID.count(mosesPrevHypothesisID) == 0) {
mosesIDToHypergraphID[mosesPrevHypothesisID] = hypergraphHypothesisID;
// hypergraphIDToMosesID[hypergraphHypothesisID] = mosesPrevHypothesisID;
hypergraphHypothesisID += 1;
}
}
// Get an id number for this hypothesis
int mosesHypothesisID;
if (searchGraph[arcNumber].recombinationHypo) {
mosesHypothesisID = searchGraph[arcNumber].recombinationHypo->GetId();
} else {
mosesHypothesisID = searchGraph[arcNumber].hypo->GetId();
}
if (mosesIDToHypergraphID.count(mosesHypothesisID) == 0) {
mosesIDToHypergraphID[mosesHypothesisID] = hypergraphHypothesisID;
// hypergraphIDToMosesID[hypergraphHypothesisID] = mosesHypothesisID;
bool terminalNode = (searchGraph[arcNumber].forward == -1);
if (terminalNode) {
// Final arc to end node, representing the end of the sentence </s>
terminalNodes.insert(hypergraphHypothesisID);
}
hypergraphHypothesisID += 1;
}
// Record that this arc ends at this node
hypergraphIDToArcs.insert(pair<int,int>(mosesIDToHypergraphID[mosesHypothesisID],arcNumber));
}
// Unique end node
endNode = hypergraphHypothesisID;
// mosesIDToHypergraphID[hypergraphHypothesisID] = hypergraphHypothesisID;
numNodes = endNode + 1;
}
long numArcs = searchGraph.size() + terminalNodes.size();
// Print number of nodes and arcs
outputSearchGraphStream << numNodes << " " << numArcs << endl;
VERBOSE(2,"Search graph to output as hypergraph for sentence " << translationId
<< " contains " << numArcs << " arcs and " << numNodes << " nodes" << std::endl)
VERBOSE(2,"Outputting search graph to output as hypergraph for sentence " << translationId << std::endl)
for (int hypergraphHypothesisID=0; hypergraphHypothesisID < endNode; hypergraphHypothesisID+=1) {
if (hypergraphHypothesisID % 100000 == 0) {
VERBOSE(2,"Processed " << hypergraphHypothesisID << " of " << numNodes << " hypergraph nodes for sentence " << translationId << std::endl);
}
// int mosesID = hypergraphIDToMosesID[hypergraphHypothesisID];
size_t count = hypergraphIDToArcs.count(hypergraphHypothesisID);
// VERBOSE(2,"Hypergraph node " << hypergraphHypothesisID << " has " << count << " incoming arcs" << std::endl)
if (count > 0) {
outputSearchGraphStream << count << "\n";
pair<multimap<int,int>::iterator, multimap<int,int>::iterator> range =
hypergraphIDToArcs.equal_range(hypergraphHypothesisID);
for (multimap<int,int>::iterator it=range.first; it!=range.second; ++it) {
int lineNumber = (*it).second;
const Hypothesis *thisHypo = searchGraph[lineNumber].hypo;
int mosesHypothesisID;// = thisHypo->GetId();
if (searchGraph[lineNumber].recombinationHypo) {
mosesHypothesisID = searchGraph[lineNumber].recombinationHypo->GetId();
} else {
mosesHypothesisID = searchGraph[lineNumber].hypo->GetId();
}
// int actualHypergraphHypothesisID = mosesIDToHypergraphID[mosesHypothesisID];
UTIL_THROW_IF(
(hypergraphHypothesisID != mosesIDToHypergraphID[mosesHypothesisID]),
util::Exception,
"Error while writing search lattice as hypergraph for sentence " << translationId << ". " <<
"Moses node " << mosesHypothesisID << " was expected to have hypergraph id " << hypergraphHypothesisID <<
", but actually had hypergraph id " << mosesIDToHypergraphID[mosesHypothesisID] <<
". There are " << numNodes << " nodes in the search lattice."
);
const Hypothesis *prevHypo = thisHypo->GetPrevHypo();
if (prevHypo==NULL) {
// VERBOSE(2,"Hypergraph node " << hypergraphHypothesisID << " start of sentence" << std::endl)
outputSearchGraphStream << "<s> ||| \n";
} else {
int startNode = mosesIDToHypergraphID[prevHypo->GetId()];
// VERBOSE(2,"Hypergraph node " << hypergraphHypothesisID << " has parent node " << startNode << std::endl)
UTIL_THROW_IF(
(startNode >= hypergraphHypothesisID),
util::Exception,
"Error while writing search lattice as hypergraph for sentence" << translationId << ". " <<
"The nodes must be output in topological order. The code attempted to violate this restriction."
);
const TargetPhrase &targetPhrase = thisHypo->GetCurrTargetPhrase();
int targetWordCount = targetPhrase.GetSize();
outputSearchGraphStream << "[" << startNode << "]";
for (int targetWordIndex=0; targetWordIndex<targetWordCount; targetWordIndex+=1) {
outputSearchGraphStream << " " << targetPhrase.GetWord(targetWordIndex);
}
outputSearchGraphStream << " ||| ";
OutputFeatureValuesForHypergraph(thisHypo, outputSearchGraphStream);
outputSearchGraphStream << "\n";
}
}
}
}
// Print node and arc(s) for end of sentence </s>
outputSearchGraphStream << terminalNodes.size() << "\n";
for (set<int>::iterator it=terminalNodes.begin(); it!=terminalNodes.end(); ++it) {
outputSearchGraphStream << "[" << (*it) << "] </s> ||| \n";
}
}
/**! Output search graph in HTK standard lattice format (SLF) */
void Manager::OutputSearchGraphAsSLF(long translationId, std::ostream &outputSearchGraphStream) const
{
vector<SearchGraphNode> searchGraph;
GetSearchGraph(searchGraph);
long numArcs = 0;
long numNodes = 0;
map<int,int> nodes;
set<int> terminalNodes;
// Unique start node
nodes[0] = 0;
for (size_t arcNumber = 0; arcNumber < searchGraph.size(); ++arcNumber) {
int targetWordCount = searchGraph[arcNumber].hypo->GetCurrTargetPhrase().GetSize();
numArcs += targetWordCount;
int hypothesisID = searchGraph[arcNumber].hypo->GetId();
if (nodes.count(hypothesisID) == 0) {
numNodes += targetWordCount;
nodes[hypothesisID] = numNodes;
//numNodes += 1;
bool terminalNode = (searchGraph[arcNumber].forward == -1);
if (terminalNode) {
numArcs += 1;
}
}
}
numNodes += 1;
// Unique end node
nodes[numNodes] = numNodes;
outputSearchGraphStream << "UTTERANCE=Sentence_" << translationId << endl;
outputSearchGraphStream << "VERSION=1.1" << endl;
outputSearchGraphStream << "base=2.71828182845905" << endl;
outputSearchGraphStream << "NODES=" << (numNodes+1) << endl;
outputSearchGraphStream << "LINKS=" << numArcs << endl;
OutputFeatureWeightsForSLF(outputSearchGraphStream);
for (size_t arcNumber = 0, lineNumber = 0; lineNumber < searchGraph.size(); ++lineNumber) {
const Hypothesis *thisHypo = searchGraph[lineNumber].hypo;
const Hypothesis *prevHypo = thisHypo->GetPrevHypo();
if (prevHypo) {
int startNode = nodes[prevHypo->GetId()];
int endNode = nodes[thisHypo->GetId()];
bool terminalNode = (searchGraph[lineNumber].forward == -1);
const TargetPhrase &targetPhrase = thisHypo->GetCurrTargetPhrase();
int targetWordCount = targetPhrase.GetSize();
for (int targetWordIndex=0; targetWordIndex<targetWordCount; targetWordIndex+=1) {
int x = (targetWordCount-targetWordIndex);
outputSearchGraphStream << "J=" << arcNumber;
if (targetWordIndex==0) {
outputSearchGraphStream << " S=" << startNode;
} else {
outputSearchGraphStream << " S=" << endNode - x;
}
outputSearchGraphStream << " E=" << endNode - (x-1)
<< " W=" << targetPhrase.GetWord(targetWordIndex);
OutputFeatureValuesForSLF(thisHypo, (targetWordIndex>0), outputSearchGraphStream);
outputSearchGraphStream << endl;
arcNumber += 1;
}
if (terminalNode && terminalNodes.count(endNode) == 0) {
terminalNodes.insert(endNode);
outputSearchGraphStream << "J=" << arcNumber
<< " S=" << endNode
<< " E=" << numNodes
<< endl;
arcNumber += 1;
}
}
}
}
void OutputSearchNode(long translationId, std::ostream &outputSearchGraphStream,
const SearchGraphNode& searchNode)
{
const vector<FactorType> &outputFactorOrder = StaticData::Instance().GetOutputFactorOrder();
bool extendedFormat = StaticData::Instance().GetOutputSearchGraphExtended();
outputSearchGraphStream << translationId;
// special case: initial hypothesis
if ( searchNode.hypo->GetId() == 0 ) {
outputSearchGraphStream << " hyp=0 stack=0";
if (extendedFormat) {
outputSearchGraphStream << " forward=" << searchNode.forward << " fscore=" << searchNode.fscore;
}
outputSearchGraphStream << endl;
return;
}
const Hypothesis *prevHypo = searchNode.hypo->GetPrevHypo();
// output in traditional format
if (!extendedFormat) {
outputSearchGraphStream << " hyp=" << searchNode.hypo->GetId()
<< " stack=" << searchNode.hypo->GetWordsBitmap().GetNumWordsCovered()
<< " back=" << prevHypo->GetId()
<< " score=" << searchNode.hypo->GetScore()
<< " transition=" << (searchNode.hypo->GetScore() - prevHypo->GetScore());
if (searchNode.recombinationHypo != NULL)
outputSearchGraphStream << " recombined=" << searchNode.recombinationHypo->GetId();
outputSearchGraphStream << " forward=" << searchNode.forward << " fscore=" << searchNode.fscore
<< " covered=" << searchNode.hypo->GetCurrSourceWordsRange().GetStartPos()
<< "-" << searchNode.hypo->GetCurrSourceWordsRange().GetEndPos()
<< " out=" << searchNode.hypo->GetCurrTargetPhrase().GetStringRep(outputFactorOrder)
<< endl;
return;
}
// output in extended format
// if (searchNode.recombinationHypo != NULL)
// outputSearchGraphStream << " hyp=" << searchNode.recombinationHypo->GetId();
// else
outputSearchGraphStream << " hyp=" << searchNode.hypo->GetId();
outputSearchGraphStream << " stack=" << searchNode.hypo->GetWordsBitmap().GetNumWordsCovered()
<< " back=" << prevHypo->GetId()
<< " score=" << searchNode.hypo->GetScore()
<< " transition=" << (searchNode.hypo->GetScore() - prevHypo->GetScore());
if (searchNode.recombinationHypo != NULL)
outputSearchGraphStream << " recombined=" << searchNode.recombinationHypo->GetId();
outputSearchGraphStream << " forward=" << searchNode.forward << " fscore=" << searchNode.fscore
<< " covered=" << searchNode.hypo->GetCurrSourceWordsRange().GetStartPos()
<< "-" << searchNode.hypo->GetCurrSourceWordsRange().GetEndPos();
// Modified so that -osgx is a superset of -osg (GST Oct 2011)
ScoreComponentCollection scoreBreakdown = searchNode.hypo->GetScoreBreakdown();
scoreBreakdown.MinusEquals( prevHypo->GetScoreBreakdown() );
//outputSearchGraphStream << " scores = [ " << StaticData::Instance().GetAllWeights();
outputSearchGraphStream << " scores=\"" << scoreBreakdown << "\"";
outputSearchGraphStream << " out=\"" << searchNode.hypo->GetSourcePhraseStringRep() << "|" <<
searchNode.hypo->GetCurrTargetPhrase().GetStringRep(outputFactorOrder) << "\"" << endl;
// outputSearchGraphStream << " out=" << searchNode.hypo->GetCurrTargetPhrase().GetStringRep(outputFactorOrder) << endl;
}
void Manager::GetConnectedGraph(
std::map< int, bool >* pConnected,
std::vector< const Hypothesis* >* pConnectedList) const
{
std::map < int, bool >& connected = *pConnected;
std::vector< const Hypothesis *>& connectedList = *pConnectedList;
// start with the ones in the final stack
const std::vector < HypothesisStack* > &hypoStackColl = m_search->GetHypothesisStacks();
const HypothesisStack &finalStack = *hypoStackColl.back();
HypothesisStack::const_iterator iterHypo;
for (iterHypo = finalStack.begin() ; iterHypo != finalStack.end() ; ++iterHypo) {
const Hypothesis *hypo = *iterHypo;
connected[ hypo->GetId() ] = true;
connectedList.push_back( hypo );
}
// move back from known connected hypotheses
for(size_t i=0; i<connectedList.size(); i++) {
const Hypothesis *hypo = connectedList[i];
// add back pointer
const Hypothesis *prevHypo = hypo->GetPrevHypo();
if (prevHypo && prevHypo->GetId() > 0 // don't add empty hypothesis
&& connected.find( prevHypo->GetId() ) == connected.end()) { // don't add already added
connected[ prevHypo->GetId() ] = true;
connectedList.push_back( prevHypo );
}
// add arcs
const ArcList *arcList = hypo->GetArcList();
if (arcList != NULL) {
ArcList::const_iterator iterArcList;
for (iterArcList = arcList->begin() ; iterArcList != arcList->end() ; ++iterArcList) {
const Hypothesis *loserHypo = *iterArcList;
if (connected.find( loserHypo->GetId() ) == connected.end()) { // don't add already added
connected[ loserHypo->GetId() ] = true;
connectedList.push_back( loserHypo );
}
}
}
}
}
void Manager::GetWinnerConnectedGraph(
std::map< int, bool >* pConnected,
std::vector< const Hypothesis* >* pConnectedList) const
{
std::map < int, bool >& connected = *pConnected;
std::vector< const Hypothesis *>& connectedList = *pConnectedList;
// start with the ones in the final stack
const std::vector < HypothesisStack* > &hypoStackColl = m_search->GetHypothesisStacks();
const HypothesisStack &finalStack = *hypoStackColl.back();
HypothesisStack::const_iterator iterHypo;
for (iterHypo = finalStack.begin() ; iterHypo != finalStack.end() ; ++iterHypo) {
const Hypothesis *hypo = *iterHypo;
connected[ hypo->GetId() ] = true;
connectedList.push_back( hypo );
}
// move back from known connected hypotheses
for(size_t i=0; i<connectedList.size(); i++) {
const Hypothesis *hypo = connectedList[i];
// add back pointer
const Hypothesis *prevHypo = hypo->GetPrevHypo();
if (prevHypo->GetId() > 0 // don't add empty hypothesis
&& connected.find( prevHypo->GetId() ) == connected.end()) { // don't add already added
connected[ prevHypo->GetId() ] = true;
connectedList.push_back( prevHypo );
}
// add arcs
const ArcList *arcList = hypo->GetArcList();
if (arcList != NULL) {
ArcList::const_iterator iterArcList;
for (iterArcList = arcList->begin() ; iterArcList != arcList->end() ; ++iterArcList) {
const Hypothesis *loserHypo = *iterArcList;
if (connected.find( loserHypo->GetPrevHypo()->GetId() ) == connected.end() && loserHypo->GetPrevHypo()->GetId() > 0) { // don't add already added & don't add hyp 0
connected[ loserHypo->GetPrevHypo()->GetId() ] = true;
connectedList.push_back( loserHypo->GetPrevHypo() );
}
}
}
}
}
#ifdef HAVE_PROTOBUF
void SerializeEdgeInfo(const Hypothesis* hypo, hgmert::Hypergraph_Edge* edge)
{
hgmert::Rule* rule = edge->mutable_rule();
hypo->GetCurrTargetPhrase().WriteToRulePB(rule);
const Hypothesis* prev = hypo->GetPrevHypo();
// if the feature values are empty, they default to 0
if (!prev) return;
// score breakdown is an aggregate (forward) quantity, but the exported
// graph object just wants the feature values on the edges
const ScoreComponentCollection& scores = hypo->GetScoreBreakdown();
const ScoreComponentCollection& pscores = prev->GetScoreBreakdown();
for (unsigned int i = 0; i < scores.size(); ++i)
edge->add_feature_values((scores[i] - pscores[i]) * -1.0);
}
hgmert::Hypergraph_Node* GetHGNode(
const Hypothesis* hypo,
std::map< int, int>* i2hgnode,
hgmert::Hypergraph* hg,
int* hgNodeIdx)
{
hgmert::Hypergraph_Node* hgnode;
std::map < int, int >::iterator idxi = i2hgnode->find(hypo->GetId());
if (idxi == i2hgnode->end()) {
*hgNodeIdx = ((*i2hgnode)[hypo->GetId()] = hg->nodes_size());
hgnode = hg->add_nodes();
} else {
*hgNodeIdx = idxi->second;
hgnode = hg->mutable_nodes(*hgNodeIdx);
}
return hgnode;
}
void Manager::SerializeSearchGraphPB(
long translationId,
std::ostream& outputStream) const
{
using namespace hgmert;
std::map < int, bool > connected;
std::map < int, int > i2hgnode;
std::vector< const Hypothesis *> connectedList;
GetConnectedGraph(&connected, &connectedList);
connected[ 0 ] = true;
Hypergraph hg;
hg.set_is_sorted(false);
int num_feats = (*m_search->GetHypothesisStacks().back()->begin())->GetScoreBreakdown().size();
hg.set_num_features(num_feats);
StaticData::Instance().GetScoreIndexManager().SerializeFeatureNamesToPB(&hg);
Hypergraph_Node* goal = hg.add_nodes(); // idx=0 goal node must have idx 0
Hypergraph_Node* source = hg.add_nodes(); // idx=1
i2hgnode[-1] = 1; // source node
const std::vector < HypothesisStack* > &hypoStackColl = m_search->GetHypothesisStacks();
const HypothesisStack &finalStack = *hypoStackColl.back();
for (std::vector < HypothesisStack* >::const_iterator iterStack = hypoStackColl.begin();
iterStack != hypoStackColl.end() ; ++iterStack) {
const HypothesisStack &stack = **iterStack;
HypothesisStack::const_iterator iterHypo;
for (iterHypo = stack.begin() ; iterHypo != stack.end() ; ++iterHypo) {
const Hypothesis *hypo = *iterHypo;
bool is_goal = hypo->GetWordsBitmap().IsComplete();
if (connected.find( hypo->GetId() ) != connected.end()) {
int headNodeIdx;
Hypergraph_Node* headNode = GetHGNode(hypo, &i2hgnode, &hg, &headNodeIdx);
if (is_goal) {
Hypergraph_Edge* ge = hg.add_edges();
ge->set_head_node(0); // goal
ge->add_tail_nodes(headNodeIdx);
ge->mutable_rule()->add_trg_words("[X,1]");
}
Hypergraph_Edge* edge = hg.add_edges();
SerializeEdgeInfo(hypo, edge);
edge->set_head_node(headNodeIdx);
const Hypothesis* prev = hypo->GetPrevHypo();
int tailNodeIdx = 1; // source
if (prev)
tailNodeIdx = i2hgnode.find(prev->GetId())->second;
edge->add_tail_nodes(tailNodeIdx);
const ArcList *arcList = hypo->GetArcList();
if (arcList != NULL) {
ArcList::const_iterator iterArcList;
for (iterArcList = arcList->begin() ; iterArcList != arcList->end() ; ++iterArcList) {
const Hypothesis *loserHypo = *iterArcList;
CHECK(connected[loserHypo->GetId()]);
Hypergraph_Edge* edge = hg.add_edges();
SerializeEdgeInfo(loserHypo, edge);
edge->set_head_node(headNodeIdx);
tailNodeIdx = i2hgnode.find(loserHypo->GetPrevHypo()->GetId())->second;
edge->add_tail_nodes(tailNodeIdx);
}
} // end if arcList empty
} // end if connected
} // end for iterHypo
} // end for iterStack
hg.SerializeToOstream(&outputStream);
}
#endif
void Manager::OutputSearchGraph(long translationId, std::ostream &outputSearchGraphStream) const
{
vector<SearchGraphNode> searchGraph;
GetSearchGraph(searchGraph);
for (size_t i = 0; i < searchGraph.size(); ++i) {
OutputSearchNode(translationId,outputSearchGraphStream,searchGraph[i]);
}
}
void Manager::GetForwardBackwardSearchGraph(std::map< int, bool >* pConnected,
std::vector< const Hypothesis* >* pConnectedList, std::map < const Hypothesis*, set< const Hypothesis* > >* pOutgoingHyps, vector< float>* pFwdBwdScores) const
{
std::map < int, bool > &connected = *pConnected;
std::vector< const Hypothesis *>& connectedList = *pConnectedList;
std::map < int, int > forward;
std::map < int, double > forwardScore;
std::map < const Hypothesis*, set <const Hypothesis*> > & outgoingHyps = *pOutgoingHyps;
vector< float> & estimatedScores = *pFwdBwdScores;
// *** find connected hypotheses ***
GetWinnerConnectedGraph(&connected, &connectedList);
// ** compute best forward path for each hypothesis *** //
// forward cost of hypotheses on final stack is 0
const std::vector < HypothesisStack* > &hypoStackColl = m_search->GetHypothesisStacks();
const HypothesisStack &finalStack = *hypoStackColl.back();
HypothesisStack::const_iterator iterHypo;
for (iterHypo = finalStack.begin() ; iterHypo != finalStack.end() ; ++iterHypo) {
const Hypothesis *hypo = *iterHypo;
forwardScore[ hypo->GetId() ] = 0.0f;
forward[ hypo->GetId() ] = -1;
}
// compete for best forward score of previous hypothesis
std::vector < HypothesisStack* >::const_iterator iterStack;
for (iterStack = --hypoStackColl.end() ; iterStack != hypoStackColl.begin() ; --iterStack) {
const HypothesisStack &stack = **iterStack;
HypothesisStack::const_iterator iterHypo;
for (iterHypo = stack.begin() ; iterHypo != stack.end() ; ++iterHypo) {
const Hypothesis *hypo = *iterHypo;
if (connected.find( hypo->GetId() ) != connected.end()) {
// make a play for previous hypothesis
const Hypothesis *prevHypo = hypo->GetPrevHypo();
double fscore = forwardScore[ hypo->GetId() ] +
hypo->GetScore() - prevHypo->GetScore();
if (forwardScore.find( prevHypo->GetId() ) == forwardScore.end()
|| forwardScore.find( prevHypo->GetId() )->second < fscore) {
forwardScore[ prevHypo->GetId() ] = fscore;
forward[ prevHypo->GetId() ] = hypo->GetId();
}
//store outgoing info
outgoingHyps[prevHypo].insert(hypo);
// all arcs also make a play
const ArcList *arcList = hypo->GetArcList();
if (arcList != NULL) {
ArcList::const_iterator iterArcList;
for (iterArcList = arcList->begin() ; iterArcList != arcList->end() ; ++iterArcList) {
const Hypothesis *loserHypo = *iterArcList;
// make a play
const Hypothesis *loserPrevHypo = loserHypo->GetPrevHypo();
double fscore = forwardScore[ hypo->GetId() ] +
loserHypo->GetScore() - loserPrevHypo->GetScore();
if (forwardScore.find( loserPrevHypo->GetId() ) == forwardScore.end()
|| forwardScore.find( loserPrevHypo->GetId() )->second < fscore) {
forwardScore[ loserPrevHypo->GetId() ] = fscore;
forward[ loserPrevHypo->GetId() ] = loserHypo->GetId();
}
//store outgoing info
outgoingHyps[loserPrevHypo].insert(hypo);
} // end for arc list
} // end if arc list empty
} // end if hypo connected
} // end for hypo
} // end for stack
for (std::vector< const Hypothesis *>::iterator it = connectedList.begin(); it != connectedList.end(); ++it) {
float estimatedScore = (*it)->GetScore() + forwardScore[(*it)->GetId()];
estimatedScores.push_back(estimatedScore);
}
}
const Hypothesis *Manager::GetBestHypothesis() const
{
return m_search->GetBestHypothesis();
}
int Manager::GetNextHypoId()
{
return m_hypoId++;
}
void Manager::ResetSentenceStats(const InputType& source)
{
m_sentenceStats = std::auto_ptr<SentenceStats>(new SentenceStats(source));
}
SentenceStats& Manager::GetSentenceStats() const
{
return *m_sentenceStats;
}
}
Reference in New Issue View Git Blame Copy Permalink