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mosesdecoder/moses/TranslationModel/PhraseDictionaryMultiModelCounts.cpp

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/***********************************************************************
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
***********************************************************************/
#include "util/exception.hh"
#include "moses/TranslationModel/PhraseDictionaryMultiModelCounts.h"
#define LINE_MAX_LENGTH 100000
#include "phrase-extract/SafeGetline.h" // for SAFE_GETLINE()
using namespace std;
template<typename T>
void OutputVec(const vector<T> &vec)
{
for (size_t i = 0; i < vec.size(); ++i) {
cerr << vec[i] << " " << flush;
}
cerr << endl;
}
// from phrase-extract/tables-core.cpp
vector<string> tokenize( const char* input )
{
vector< string > token;
bool betweenWords = true;
int start=0;
int i=0;
for(; input[i] != '\0'; i++) {
bool isSpace = (input[i] == ' ' || input[i] == '\t');
if (!isSpace && betweenWords) {
start = i;
betweenWords = false;
} else if (isSpace && !betweenWords) {
token.push_back( string( input+start, i-start ) );
betweenWords = true;
}
}
if (!betweenWords)
token.push_back( string( input+start, i-start ) );
return token;
}
namespace Moses
{
PhraseDictionaryMultiModelCounts::PhraseDictionaryMultiModelCounts(const std::string &line)
:PhraseDictionaryMultiModel("PhraseDictionaryMultiModelCounts", line)
{
m_mode = "instance_weighting"; //TODO: set this in config; use m_mode to switch between interpolation and instance weighting
m_combineFunction = InstanceWeighting;
//m_mode = "interpolate";
//m_combineFunction = LinearInterpolationFromCounts;
for (size_t i = 0; i < m_args.size(); ++i) {
const vector<string> &args = m_args[i];
if (args[0] == "mode") {
m_mode = args[1];
if (m_mode == "instance_weighting")
m_combineFunction = InstanceWeighting;
else if (m_mode == "interpolate") {
m_combineFunction = LinearInterpolationFromCounts;
}
else {
ostringstream msg;
msg << "combination mode unknown: " << m_mode;
throw runtime_error(msg.str());
}
}
else if (args[0] == "lex-e2f") {
m_lexE2FStr = Tokenize(args[1], ",");
CHECK(m_lexE2FStr.size() == m_pdStr.size());
}
else if (args[0] == "lex-f2e") {
m_lexF2EStr = Tokenize(args[1], ",");
CHECK(m_lexF2EStr.size() == m_pdStr.size());
}
else if (args[0] == "target-table") {
m_targetTable = Tokenize(args[1], ",");
CHECK(m_targetTable.size() == m_pdStr.size());
}
} // for
}
PhraseDictionaryMultiModelCounts::~PhraseDictionaryMultiModelCounts()
{
RemoveAllInColl(m_lexTable_e2f);
RemoveAllInColl(m_lexTable_f2e);
}
bool PhraseDictionaryMultiModelCounts::InitDictionary()
{
for(size_t i = 0; i < m_numModels; ++i){
// phrase table
const string &ptName = m_pdStr[i];
PhraseDictionary *pt;
pt = FindPhraseDictionary(ptName);
CHECK(pt);
m_pd.push_back(pt);
// reverse
const string &target_table = m_targetTable[i];
pt = FindPhraseDictionary(target_table);
CHECK(pt);
m_inverse_pd.push_back(pt);
// lex
string lex_e2f = m_lexE2FStr[i];
string lex_f2e = m_lexF2EStr[i];
lexicalTable* e2f = new lexicalTable;
LoadLexicalTable(lex_e2f, e2f);
lexicalTable* f2e = new lexicalTable;
LoadLexicalTable(lex_f2e, f2e);
m_lexTable_e2f.push_back(e2f);
m_lexTable_f2e.push_back(f2e);
}
/*
for(size_t i = 0; i < m_numModels; ++i){
string impl, file, main_table, target_table, lex_e2f, lex_f2e;
string delim = ":";
size_t delim_pos = files[i].find(delim);
UTIL_THROW_IF(delim_pos >= files[i].size(), util::Exception, "Phrase table must be specified in this format: Implementation:Path");
impl = files[i].substr(0,delim_pos);
file = files[i].substr(delim_pos+1,files[i].size());
main_table = file + "/count-table";
target_table = file + "/count-table-target";
lex_e2f = file + "/lex.counts.e2f";
lex_f2e = file + "/lex.counts.f2e";
size_t componentTableLimit = 0; // using 0, because we can't trust implemented pruning algorithms with count tables.
PhraseTableImplementation implementation = (PhraseTableImplementation) Scan<int>(impl);
//how many actual scores there are in the phrase tables
size_t numScoresCounts = 3;
size_t numScoresTargetCounts = 1;
if (implementation == Memory) {
if (!FileExists(main_table) && FileExists(main_table + ".gz")) main_table += ".gz";
if (!FileExists(target_table) && FileExists(target_table + ".gz")) target_table += ".gz";
PhraseDictionaryMemory* pdm = new PhraseDictionaryMemory(m_numScoreComponent, m_feature_load);
pdm->SetNumScoreComponentMultiModel(numScoresCounts); //instead of complaining about inequal number of scores, silently fill up the score vector with zeroes
pdm->Load( input, output, main_table, m_weight, componentTableLimit, languageModels, m_weightWP);
m_pd.push_back(pdm);
PhraseDictionaryMemory* pdm_inverse = new PhraseDictionaryMemory(m_numScoreComponent, m_feature_load);
pdm_inverse->SetNumScoreComponentMultiModel(numScoresTargetCounts);
pdm_inverse->Load( input, output, target_table, m_weight, componentTableLimit, languageModels, m_weightWP);
m_inverse_pd.push_back(pdm_inverse);
} else if (implementation == Binary) {
PhraseDictionaryTreeAdaptor* pdta = new PhraseDictionaryTreeAdaptor(m_numScoreComponent, numInputScores , m_feature_load);
pdta->SetNumScoreComponentMultiModel(m_numScoreComponent); //for binary models, we need to pass number of log-linear components to correctly resize the score vector
pdta->Load(input, output, main_table, m_weight, m_componentTableLimit, languageModels, m_weightWP);
m_pd.push_back(pdta);
PhraseDictionaryTreeAdaptor* pdta_inverse = new PhraseDictionaryTreeAdaptor(m_numScoreComponent, numInputScores , m_feature_load);
pdta_inverse->SetNumScoreComponentMultiModel(m_numScoreComponent);
pdta_inverse->Load(input, output, target_table, m_weight, m_componentTableLimit, languageModels, m_weightWP);
m_inverse_pd.push_back(pdta_inverse);
} else if (implementation == Compact) {
#ifndef WIN32
PhraseDictionaryCompact* pdc = new PhraseDictionaryCompact(m_numScoreComponent, implementation, m_feature_load);
pdc->SetNumScoreComponentMultiModel(m_numScoreComponent); //for compact models, we need to pass number of log-linear components to correctly resize the score vector
pdc->Load( input, output, main_table, m_weight, componentTableLimit, languageModels, m_weightWP);
m_pd.push_back(pdc);
PhraseDictionaryCompact* pdc_inverse = new PhraseDictionaryCompact(m_numScoreComponent, implementation, m_feature_load);
pdc_inverse->SetNumScoreComponentMultiModel(m_numScoreComponent);
pdc_inverse->Load( input, output, target_table, m_weight, componentTableLimit, languageModels, m_weightWP);
m_inverse_pd.push_back(pdc_inverse);
#else
UTIL_THROW(util::Exception, "Compact phrase table not supported in windows");
#endif
}
else {
UTIL_THROW(util::Exception,"PhraseDictionaryMultiModel does not support phrase table type " << implementation);
}
lexicalTable* e2f = new lexicalTable;
LoadLexicalTable(lex_e2f, e2f);
lexicalTable* f2e = new lexicalTable;
LoadLexicalTable(lex_f2e, f2e);
m_lexTable_e2f.push_back(e2f);
m_lexTable_f2e.push_back(f2e);
}
*/
return true;
}
const TargetPhraseCollection *PhraseDictionaryMultiModelCounts::GetTargetPhraseCollection(const Phrase& src) const
{
vector<vector<float> > multimodelweights;
bool normalize;
normalize = (m_mode == "interpolate") ? true : false;
multimodelweights = getWeights(4,normalize);
//source phrase frequency is shared among all phrase pairs
vector<float> fs(m_numModels);
map<string,multiModelCountsStatistics*>* allStats = new(map<string,multiModelCountsStatistics*>);
CollectSufficientStatistics(src, fs, allStats);
TargetPhraseCollection *ret = CreateTargetPhraseCollectionCounts(src, fs, allStats, multimodelweights);
ret->NthElement(m_tableLimit); // sort the phrases for pruning later
const_cast<PhraseDictionaryMultiModelCounts*>(this)->CacheForCleanup(ret);
return ret;
}
void PhraseDictionaryMultiModelCounts::CollectSufficientStatistics(const Phrase& src, vector<float> &fs, map<string,multiModelCountsStatistics*>* allStats) const
//fill fs and allStats with statistics from models
{
for(size_t i = 0; i < m_numModels; ++i){
const PhraseDictionary &pd = *m_pd[i];
TargetPhraseCollection *ret_raw = (TargetPhraseCollection*) pd.GetTargetPhraseCollection( src);
if (ret_raw != NULL) {
TargetPhraseCollection::iterator iterTargetPhrase;
for (iterTargetPhrase = ret_raw->begin(); iterTargetPhrase != ret_raw->end(); ++iterTargetPhrase) {
TargetPhrase * targetPhrase = *iterTargetPhrase;
vector<float> raw_scores = targetPhrase->GetScoreBreakdown().GetScoresForProducer(&pd);
string targetString = targetPhrase->GetStringRep(m_output);
if (allStats->find(targetString) == allStats->end()) {
multiModelCountsStatistics * statistics = new multiModelCountsStatistics;
statistics->targetPhrase = new TargetPhrase(*targetPhrase); //make a copy so that we don't overwrite the original phrase table info
// zero out scores from original phrase table
statistics->targetPhrase->GetScoreBreakdown().ZeroDenseFeatures(&pd);
statistics->fst.resize(m_numModels);
statistics->ft.resize(m_numModels);
Scores scoreVector(5);
scoreVector[0] = -raw_scores[0];
scoreVector[1] = -raw_scores[1];
scoreVector[2] = -raw_scores[2];
statistics->targetPhrase->GetScoreBreakdown().Assign(this, scoreVector); // set scores to 0
statistics->targetPhrase->Evaluate();
(*allStats)[targetString] = statistics;
}
multiModelCountsStatistics * statistics = (*allStats)[targetString];
statistics->fst[i] = UntransformScore(raw_scores[0]);
statistics->ft[i] = UntransformScore(raw_scores[1]);
fs[i] = UntransformScore(raw_scores[2]);
(*allStats)[targetString] = statistics;
}
}
}
// get target phrase frequency for models which have not seen the phrase pair
for ( map< string, multiModelCountsStatistics*>::const_iterator iter = allStats->begin(); iter != allStats->end(); ++iter ) {
multiModelCountsStatistics * statistics = iter->second;
for (size_t i = 0; i < m_numModels; ++i) {
if (!statistics->ft[i]) {
statistics->ft[i] = GetTargetCount(static_cast<const Phrase&>(*statistics->targetPhrase), i);
}
}
}
}
TargetPhraseCollection* PhraseDictionaryMultiModelCounts::CreateTargetPhraseCollectionCounts(const Phrase &src, vector<float> &fs, map<string,multiModelCountsStatistics*>* allStats, vector<vector<float> > &multimodelweights) const
{
TargetPhraseCollection *ret = new TargetPhraseCollection();
for ( map< string, multiModelCountsStatistics*>::const_iterator iter = allStats->begin(); iter != allStats->end(); ++iter ) {
multiModelCountsStatistics * statistics = iter->second;
if (statistics->targetPhrase->GetAlignTerm().GetSize() == 0) {
UTIL_THROW(util::Exception, " alignment information empty\ncount-tables need to include alignment information for computation of lexical weights.\nUse --phrase-word-alignment during training; for on-disk tables, also set -alignment-info when creating on-disk tables.");
}
try {
pair<vector< set<size_t> >, vector< set<size_t> > > alignment = GetAlignmentsForLexWeights(src, static_cast<const Phrase&>(*statistics->targetPhrase), statistics->targetPhrase->GetAlignTerm());
vector< set<size_t> > alignedToT = alignment.first;
vector< set<size_t> > alignedToS = alignment.second;
double lexst = ComputeWeightedLexicalTranslation(static_cast<const Phrase&>(*statistics->targetPhrase), src, alignedToS, m_lexTable_e2f, multimodelweights[1], m_output, m_input );
double lexts = ComputeWeightedLexicalTranslation(src, static_cast<const Phrase&>(*statistics->targetPhrase), alignedToT, m_lexTable_f2e, multimodelweights[3], m_input, m_output );
Scores scoreVector(5);
scoreVector[0] = FloorScore(TransformScore(m_combineFunction(statistics->fst, statistics->ft, multimodelweights[0])));
scoreVector[1] = FloorScore(TransformScore(lexst));
scoreVector[2] = FloorScore(TransformScore(m_combineFunction(statistics->fst, fs, multimodelweights[2])));
scoreVector[3] = FloorScore(TransformScore(lexts));
scoreVector[4] = FloorScore(TransformScore(2.718));
statistics->targetPhrase->GetScoreBreakdown().Assign(this, scoreVector);
statistics->targetPhrase->Evaluate();
}
catch (AlignmentException& e) {
continue;
}
ret->Add(new TargetPhrase(*statistics->targetPhrase));
}
RemoveAllInMap(*allStats);
delete allStats;
return ret;
}
float PhraseDictionaryMultiModelCounts::GetTargetCount(const Phrase &target, size_t modelIndex) const {
const PhraseDictionary &pd = *m_inverse_pd[modelIndex];
TargetPhraseCollection *ret_raw = (TargetPhraseCollection*) pd.GetTargetPhraseCollection(target);
// in inverse mode, we want the first score of the first phrase pair (note: if we were to work with truly symmetric models, it would be the third score)
if (ret_raw != NULL) {
TargetPhrase * targetPhrase = *(ret_raw->begin());
return UntransformScore(targetPhrase->GetScoreBreakdown().GetScoresForProducer(&pd)[0]);
}
// target phrase unknown
else return 0;
}
pair<PhraseDictionaryMultiModelCounts::AlignVector,PhraseDictionaryMultiModelCounts::AlignVector> PhraseDictionaryMultiModelCounts::GetAlignmentsForLexWeights(const Phrase &phraseS, const Phrase &phraseT, const AlignmentInfo &alignment) const {
size_t tsize = phraseT.GetSize();
size_t ssize = phraseS.GetSize();
AlignVector alignedToT (tsize);
AlignVector alignedToS (ssize);
AlignmentInfo::const_iterator iter;
for (iter = alignment.begin(); iter != alignment.end(); ++iter) {
const pair<size_t,size_t> &alignPair = *iter;
size_t s = alignPair.first;
size_t t = alignPair.second;
if (s >= ssize || t >= tsize) {
cerr << "Error: inconsistent alignment for phrase pair: " << phraseS << " - " << phraseT << endl;
cerr << "phrase pair will be discarded" << endl;
throw AlignmentException();
}
alignedToT[t].insert( s );
alignedToS[s].insert( t );
}
return make_pair(alignedToT,alignedToS);
}
double PhraseDictionaryMultiModelCounts::ComputeWeightedLexicalTranslation( const Phrase &phraseS, const Phrase &phraseT, AlignVector &alignment, const vector<lexicalTable*> &tables, vector<float> &multimodelweights, const vector<FactorType> &input_factors, const vector<FactorType> &output_factors ) const {
// lexical translation probability
double lexScore = 1.0;
string null = "NULL";
// all target words have to be explained
for(size_t ti=0; ti<alignment.size(); ti++) {
const set< size_t > & srcIndices = alignment[ ti ];
Word t_word = phraseT.GetWord(ti);
string ti_str = t_word.GetString(output_factors, false);
if (srcIndices.empty()) {
// explain unaligned word by NULL
lexScore *= GetLexicalProbability( null, ti_str, tables, multimodelweights );
} else {
// go through all the aligned words to compute average
double thisWordScore = 0;
for (set< size_t >::const_iterator si(srcIndices.begin()); si != srcIndices.end(); ++si) {
string s_str = phraseS.GetWord(*si).GetString(input_factors, false);
thisWordScore += GetLexicalProbability( s_str, ti_str, tables, multimodelweights );
}
lexScore *= thisWordScore / srcIndices.size();
}
}
return lexScore;
}
lexicalCache PhraseDictionaryMultiModelCounts::CacheLexicalStatistics( const Phrase &phraseS, const Phrase &phraseT, AlignVector &alignment, const vector<lexicalTable*> &tables, const vector<FactorType> &input_factors, const vector<FactorType> &output_factors ) {
//do all the necessary lexical table lookups and get counts, but don't apply weights yet
string null = "NULL";
lexicalCache ret;
// all target words have to be explained
for(size_t ti=0; ti<alignment.size(); ti++) {
const set< size_t > & srcIndices = alignment[ ti ];
Word t_word = phraseT.GetWord(ti);
string ti_str = t_word.GetString(output_factors, false);
vector<lexicalPair> ti_vector;
if (srcIndices.empty()) {
// explain unaligned word by NULL
vector<float> joint_count (m_numModels);
vector<float> marginals (m_numModels);
FillLexicalCountsJoint(null, ti_str, joint_count, tables);
FillLexicalCountsMarginal(null, marginals, tables);
ti_vector.push_back(make_pair(joint_count, marginals));
} else {
for (set< size_t >::const_iterator si(srcIndices.begin()); si != srcIndices.end(); ++si) {
string s_str = phraseS.GetWord(*si).GetString(input_factors, false);
vector<float> joint_count (m_numModels);
vector<float> marginals (m_numModels);
FillLexicalCountsJoint(s_str, ti_str, joint_count, tables);
FillLexicalCountsMarginal(s_str, marginals, tables);
ti_vector.push_back(make_pair(joint_count, marginals));
}
}
ret.push_back(ti_vector);
}
return ret;
}
double PhraseDictionaryMultiModelCounts::ComputeWeightedLexicalTranslationFromCache( lexicalCache &cache, vector<float> &weights ) const {
// lexical translation probability
double lexScore = 1.0;
for (lexicalCache::const_iterator iter = cache.begin(); iter != cache.end(); ++iter) {
vector<lexicalPair> t_vector = *iter;
double thisWordScore = 0;
for ( vector<lexicalPair>::const_iterator iter2 = t_vector.begin(); iter2 != t_vector.end(); ++iter2) {
vector<float> joint_count = iter2->first;
vector<float> marginal = iter2->second;
thisWordScore += m_combineFunction(joint_count, marginal, weights);
}
lexScore *= thisWordScore / t_vector.size();
}
return lexScore;
}
// get lexical probability for single word alignment pair
double PhraseDictionaryMultiModelCounts::GetLexicalProbability( string &wordS, string &wordT, const vector<lexicalTable*> &tables, vector<float> &multimodelweights ) const {
vector<float> joint_count (m_numModels);
vector<float> marginals (m_numModels);
FillLexicalCountsJoint(wordS, wordT, joint_count, tables);
FillLexicalCountsMarginal(wordS, marginals, tables);
double lexProb = m_combineFunction(joint_count, marginals, multimodelweights);
return lexProb;
}
void PhraseDictionaryMultiModelCounts::FillLexicalCountsJoint(string &wordS, string &wordT, vector<float> &count, const vector<lexicalTable*> &tables) const {
for (size_t i=0;i < m_numModels;i++) {
lexicalMapJoint::iterator joint_s = tables[i]->joint.find( wordS );
if (joint_s == tables[i]->joint.end()) count[i] = 0.0;
else {
lexicalMap::iterator joint_t = joint_s->second.find( wordT );
if (joint_t == joint_s->second.end()) count[i] = 0.0;
else count[i] = joint_t->second;
}
}
}
void PhraseDictionaryMultiModelCounts::FillLexicalCountsMarginal(string &wordS, vector<float> &count, const vector<lexicalTable*> &tables) const {
for (size_t i=0;i < m_numModels;i++) {
lexicalMap::iterator marginal_s = tables[i]->marginal.find( wordS );
if (marginal_s == tables[i]->marginal.end()) count[i] = 0.0;
else count[i] = marginal_s->second;
}
}
void PhraseDictionaryMultiModelCounts::LoadLexicalTable( string &fileName, lexicalTable* ltable) {
cerr << "Loading lexical translation table from " << fileName;
ifstream inFile;
inFile.open(fileName.c_str());
if (inFile.fail()) {
cerr << " - ERROR: could not open file\n";
exit(1);
}
istream *inFileP = &inFile;
char line[LINE_MAX_LENGTH];
int i=0;
while(true) {
i++;
if (i%100000 == 0) cerr << "." << flush;
SAFE_GETLINE((*inFileP), line, LINE_MAX_LENGTH, '\n', __FILE__);
if (inFileP->eof()) break;
vector<string> token = tokenize( line );
if (token.size() != 4) {
cerr << "line " << i << " in " << fileName
<< " has wrong number of tokens, skipping:\n"
<< token.size() << " " << token[0] << " " << line << endl;
continue;
}
double joint = atof( token[2].c_str() );
double marginal = atof( token[3].c_str() );
string wordT = token[0];
string wordS = token[1];
ltable->joint[ wordS ][ wordT ] = joint;
ltable->marginal[ wordS ] = marginal;
}
cerr << endl;
}
#ifdef WITH_DLIB
vector<float> PhraseDictionaryMultiModelCounts::MinimizePerplexity(vector<pair<string, string> > &phrase_pair_vector) {
const StaticData &staticData = StaticData::Instance();
const string& factorDelimiter = staticData.GetFactorDelimiter();
map<pair<string, string>, size_t> phrase_pair_map;
for ( vector<pair<string, string> >::const_iterator iter = phrase_pair_vector.begin(); iter != phrase_pair_vector.end(); ++iter ) {
phrase_pair_map[*iter] += 1;
}
vector<multiModelCountsStatisticsOptimization*> optimizerStats;
for ( map<pair<string, string>, size_t>::iterator iter = phrase_pair_map.begin(); iter != phrase_pair_map.end(); ++iter ) {
pair<string, string> phrase_pair = iter->first;
string source_string = phrase_pair.first;
string target_string = phrase_pair.second;
vector<float> fs(m_numModels);
map<string,multiModelCountsStatistics*>* allStats = new(map<string,multiModelCountsStatistics*>);
Phrase sourcePhrase(0);
sourcePhrase.CreateFromString(Input, m_input, source_string, factorDelimiter, NULL);
CollectSufficientStatistics(sourcePhrase, fs, allStats); //optimization potential: only call this once per source phrase
//phrase pair not found; leave cache empty
if (allStats->find(target_string) == allStats->end()) {
RemoveAllInMap(*allStats);
delete allStats;
continue;
}
multiModelCountsStatisticsOptimization * targetStatistics = new multiModelCountsStatisticsOptimization();
targetStatistics->targetPhrase = new TargetPhrase(*(*allStats)[target_string]->targetPhrase);
targetStatistics->fs = fs;
targetStatistics->fst = (*allStats)[target_string]->fst;
targetStatistics->ft = (*allStats)[target_string]->ft;
targetStatistics->f = iter->second;
try {
pair<vector< set<size_t> >, vector< set<size_t> > > alignment = GetAlignmentsForLexWeights(sourcePhrase, static_cast<const Phrase&>(*targetStatistics->targetPhrase), targetStatistics->targetPhrase->GetAlignTerm());
targetStatistics->lexCachee2f = CacheLexicalStatistics(static_cast<const Phrase&>(*targetStatistics->targetPhrase), sourcePhrase, alignment.second, m_lexTable_e2f, m_output, m_input );
targetStatistics->lexCachef2e = CacheLexicalStatistics(sourcePhrase, static_cast<const Phrase&>(*targetStatistics->targetPhrase), alignment.first, m_lexTable_f2e, m_input, m_output );
optimizerStats.push_back(targetStatistics);
}
catch (AlignmentException& e) {}
RemoveAllInMap(*allStats);
delete allStats;
}
Sentence sentence;
CleanUpAfterSentenceProcessing(sentence); // free memory used by compact phrase tables
vector<float> ret (m_numModels*4);
for (size_t iFeature=0; iFeature < 4; iFeature++) {
CrossEntropyCounts * ObjectiveFunction = new CrossEntropyCounts(optimizerStats, this, iFeature);
vector<float> weight_vector = Optimize(ObjectiveFunction, m_numModels);
if (m_mode == "interpolate") {
weight_vector = normalizeWeights(weight_vector);
}
else if (m_mode == "instance_weighting") {
float first_value = weight_vector[0];
for (size_t i=0; i < m_numModels; i++) {
weight_vector[i] = weight_vector[i]/first_value;
}
}
cerr << "Weight vector for feature " << iFeature << ": ";
for (size_t i=0; i < m_numModels; i++) {
ret[(iFeature*m_numModels)+i] = weight_vector[i];
cerr << weight_vector[i] << " ";
}
cerr << endl;
delete ObjectiveFunction;
}
RemoveAllInColl(optimizerStats);
return ret;
}
double CrossEntropyCounts::operator() ( const dlib::matrix<double,0,1>& arg) const
{
double total = 0.0;
double n = 0.0;
std::vector<float> weight_vector (m_model->m_numModels);
for (int i=0; i < arg.nr(); i++) {
weight_vector[i] = arg(i);
}
if (m_model->m_mode == "interpolate") {
weight_vector = m_model->normalizeWeights(weight_vector);
}
for ( std::vector<multiModelCountsStatisticsOptimization*>::const_iterator iter = m_optimizerStats.begin(); iter != m_optimizerStats.end(); ++iter ) {
multiModelCountsStatisticsOptimization* statistics = *iter;
size_t f = statistics->f;
double score;
if (m_iFeature == 0) {
score = m_model->m_combineFunction(statistics->fst, statistics->ft, weight_vector);
}
else if (m_iFeature == 1) {
score = m_model->ComputeWeightedLexicalTranslationFromCache(statistics->lexCachee2f, weight_vector);
}
else if (m_iFeature == 2) {
score = m_model->m_combineFunction(statistics->fst, statistics->fs, weight_vector);
}
else if (m_iFeature == 3) {
score = m_model->ComputeWeightedLexicalTranslationFromCache(statistics->lexCachef2e, weight_vector);
}
else {
score = 0;
UTIL_THROW(util::Exception, "Trying to optimize feature that I don't know. Aborting");
}
total -= (FloorScore(TransformScore(score))/TransformScore(2))*f;
n += f;
}
return total/n;
}
#endif
// calculate weighted probability based on instance weighting of joint counts and marginal counts
double InstanceWeighting(vector<float> &joint_counts, vector<float> &marginals, vector<float> &multimodelweights) {
double joint_counts_weighted = inner_product(joint_counts.begin(), joint_counts.end(), multimodelweights.begin(), 0.0);
double marginals_weighted = inner_product(marginals.begin(), marginals.end(), multimodelweights.begin(), 0.0);
if (marginals_weighted == 0) {
return 0;
}
else {
return joint_counts_weighted/marginals_weighted;
}
}
// calculate linear interpolation of relative frequency estimates based on joint count and marginal counts
//unused for now; enable in config?
double LinearInterpolationFromCounts(vector<float> &joint_counts, vector<float> &marginals, vector<float> &multimodelweights) {
vector<float> p(marginals.size());
for (size_t i=0;i < marginals.size();i++) {
if (marginals[i] != 0) {
p[i] = joint_counts[i]/marginals[i];
}
}
double p_weighted = inner_product(p.begin(), p.end(), multimodelweights.begin(), 0.0);
return p_weighted;
}
} //namespace
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