moses-smt/mosesdecoder
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1. remove all code for MetaFeature from mira.

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2. in ShowWeights(), all print out dense feature weights. Don't print 'sparse' for sparse feature functions. All features functions can contains dense and sparse
This commit is contained in:
Hieu Hoang 2013-05-16 19:05:08 +01:00
parent b322e78a38
commit f04ec4c56d
15 changed files with 14 additions and 188 deletions
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5
contrib/other-builds/moses/.project
View File

@ -591,11 +591,6 @@
<type>1</type>
<locationURI>PARENT-3-PROJECT_LOC/moses/LexicalReorderingTable.h</locationURI>
</link>
<link>
<name>Makefile.am</name>
<type>1</type>
<locationURI>PARENT-3-PROJECT_LOC/moses/Makefile.am</locationURI>
</link>
<link>
<name>Manager.cpp</name>
<type>1</type>

118
mira/Main.cpp
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@ -524,19 +524,7 @@ int main(int argc, char** argv) {
// get reference to feature functions
const vector<FeatureFunction*> &featureFunctions = FeatureFunction::GetFeatureFunctions();
ScoreComponentCollection initialWeights = decoder->getWeights();
bool tuneMetaFeature = false;
const vector<const FeatureFunction*>& sparseProducers = staticData.GetSparseProducers();
for (unsigned i = 0; i < sparseProducers.size(); ++i) {
float spWeight = sparseProducers[i]->GetSparseProducerWeight();
if (spWeight != 1.0) {
tuneMetaFeature = true;
cerr << "Rank " << rank << ", sparse Producer " <<
sparseProducers[i]->GetScoreProducerDescription()
<< " weight: " << spWeight << endl;
}
}
if (add2lm != 0) {
const LMList& lmList_new = staticData.GetLMList();
for (LMList::const_iterator i = lmList_new.begin(); i != lmList_new.end(); ++i) {
@ -693,22 +681,6 @@ int main(int argc, char** argv) {
decoder->printBleuFeatureHistory(cerr);
}
if (tuneMetaFeature) {
// initialise meta feature
MetaFeatureProducer *m = staticData.GetMetaFeatureProducer();
FeatureFunction* ff = const_cast<FeatureFunction*>(sparseProducers[0]);
if (sparseProducers[0]->GetScoreProducerDescription().compare("wt") == 0) {
WordTranslationFeature* wt =
static_cast<WordTranslationFeature*>(ff);
mosesWeights.Assign(m, wt->GetSparseProducerWeight());
}
else if (sparseProducers[0]->GetScoreProducerDescription().compare("pp") == 0) {
PhrasePairFeature* pp =
static_cast<PhrasePairFeature*>(ff);
mosesWeights.Assign(m, pp->GetSparseProducerWeight());
}
}
// BATCHING: produce nbest lists for all input sentences in batch
vector<float> oracleBleuScores;
vector<float> oracleModelScores;
@ -1260,62 +1232,6 @@ int main(int argc, char** argv) {
}
}
if (kbest) {
// If we are tuning a global weight for a sparse producer,
// we must collapse the sparse features first (report weighted aggregate)
if (tuneMetaFeature) {
for (unsigned i = 0; i < sparseProducers.size(); ++i) {
float spWeight = sparseProducers[i]->GetSparseProducerWeight();
if (spWeight != 1.0) {
MetaFeatureProducer *m = staticData.GetMetaFeatureProducer();
for (size_t i=0; i < featureValuesHope.size(); ++i) {
for (size_t j=0; j < featureValuesHope[i].size(); ++j) {
// multiply sparse feature values with weights
const FVector scores =
featureValuesHope[i][j].GetVectorForProducer(sparseProducers[i]);
const FVector &weights = staticData.GetAllWeights().GetScoresVector();
float aggregate = scores.inner_product(weights);
//cerr << "Rank " << rank << ", epoch " << epoch << ", sparse Producer " <<
//sparseProducers[i]->GetScoreProducerDescription()
//<< " aggregate: " << aggregate << endl;
aggregate *= spWeight;
//cerr << "Rank " << rank << ", epoch " << epoch << ", sparse Producer " <<
//sparseProducers[i]->GetScoreProducerDescription()
//<< " weighted aggregate: " << aggregate << endl;
// copy core features to a new collection, then assign aggregated sparse feature
ScoreComponentCollection scoresAggregate;
scoresAggregate.CoreAssign(featureValuesHope[i][j]);
scoresAggregate.Assign(m, aggregate);
featureValuesHope[i][j] = scoresAggregate;
}
}
for (size_t i=0; i < featureValuesFear.size(); ++i) {
for (size_t j=0; j < featureValuesFear[i].size(); ++j) {
// multiply sparse feature values with weights
const FVector scores =
featureValuesFear[i][j].GetVectorForProducer(sparseProducers[i]);
const FVector &weights = staticData.GetAllWeights().GetScoresVector();
float aggregate = scores.inner_product(weights);
aggregate *= spWeight;
// copy core features to a new collection, then assign aggregated sparse feature
ScoreComponentCollection scoresAggregate;
scoresAggregate.CoreAssign(featureValuesFear[i][j]);
scoresAggregate.Assign(m, aggregate);
featureValuesFear[i][j] = scoresAggregate;
}
}
cerr << "Rank " << rank << ", epoch " << epoch << ", new hope feature vector: " <<
featureValuesHope[0][0] << endl;
cerr << "Rank " << rank << ", epoch " << epoch << ", new fear feature vector: " <<
featureValuesFear[0][0] << endl;
}
}
}
}
// Run optimiser on batch:
VERBOSE(1, "\nRank " << rank << ", epoch " << epoch << ", run optimiser:" << endl);
size_t update_status = 1;
@ -1381,34 +1297,6 @@ int main(int argc, char** argv) {
if (debug)
cerr << "Rank " << rank << ", epoch " << epoch << ", update: " << weightUpdate << endl;
if (tuneMetaFeature) {
MetaFeatureProducer *m = staticData.GetMetaFeatureProducer();
// update sparse producer weight
// (NOTE: this currently doesn't work for more than one sparse producer)
float metaWeightUpdate = weightUpdate.GetScoreForProducer(m);
const vector<const FeatureFunction*> sparseProducers = staticData.GetSparseProducers();
FeatureFunction* ff = const_cast<FeatureFunction*>(sparseProducers[0]);
if (sparseProducers[0]->GetScoreProducerDescription().compare("wt") == 0) {
WordTranslationFeature* wt =
static_cast<WordTranslationFeature*>(ff);
float newWeight = wt->GetSparseProducerWeight();
cerr << "Rank " << rank << ", epoch " << epoch << ", old meta weight: " << newWeight << endl;
newWeight += metaWeightUpdate;
wt->SetSparseProducerWeight(newWeight);
cerr << "Rank " << rank << ", epoch " << epoch << ", new meta weight: " << newWeight << endl;
}
else if (sparseProducers[0]->GetScoreProducerDescription().compare("pp") == 0) {
PhrasePairFeature* pp =
static_cast<PhrasePairFeature*>(ff);
float newWeight = pp->GetSparseProducerWeight();
cerr << "Rank " << rank << ", epoch " << epoch << ", old meta weight: " << newWeight << endl;
newWeight += metaWeightUpdate;
pp->SetSparseProducerWeight(newWeight);
cerr << "Rank " << rank << ", epoch " << epoch << ", new meta weight: " << newWeight << endl;
}
}
if (feature_confidence) {
// update confidence counts based on weight update
confidenceCounts.UpdateConfidenceCounts(weightUpdate, signed_counts);
@ -1420,7 +1308,7 @@ int main(int argc, char** argv) {
// apply weight update to Moses weights
mosesWeights.PlusEquals(weightUpdate);
if (normaliseWeights && !tuneMetaFeature)
if (normaliseWeights)
mosesWeights.L1Normalise();
cumulativeWeights.PlusEquals(mosesWeights);
@ -1432,7 +1320,7 @@ int main(int argc, char** argv) {
++numberOfUpdates;
++numberOfUpdatesThisEpoch;
if (averageWeights && !tuneMetaFeature) {
if (averageWeights) {
ScoreComponentCollection averageWeights(cumulativeWeights);
if (accumulateWeights) {
averageWeights.DivideEquals(numberOfUpdates);

19
moses-chart-cmd/Main.cpp
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@ -187,21 +187,14 @@ bool ReadInput(IOWrapper &ioWrapper, InputTypeEnum inputType, InputType*& source
}
static void PrintFeatureWeight(const FeatureFunction* ff)
{
cout << ff->GetScoreProducerDescription() << "=";
size_t numScoreComps = ff->GetNumScoreComponents();
if (numScoreComps != 0) {
vector<float> values = StaticData::Instance().GetAllWeights().GetScoresForProducer(ff);
cout << ff->GetScoreProducerDescription() << "=";
for (size_t i = 0; i < numScoreComps; ++i) {
cout << " " << values[i];
}
cout << endl;
}
else {
if (ff->GetSparseProducerWeight() == 1)
cout << ff->GetScoreProducerDescription() << "= sparse" << endl;
else
cout << ff->GetScoreProducerDescription() << "= " << ff->GetSparseProducerWeight() << endl;
vector<float> values = StaticData::Instance().GetAllWeights().GetScoresForProducer(ff);
for (size_t i = 0; i < numScoreComps; ++i) {
cout << " " << values[i];
}
cout << endl;
}
static void ShowWeights()

18
moses-cmd/Main.cpp
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@ -446,21 +446,13 @@ private:
static void PrintFeatureWeight(const FeatureFunction* ff)
{
cout << ff->GetScoreProducerDescription() << "=";
size_t numScoreComps = ff->GetNumScoreComponents();
if (numScoreComps != 0) {
vector<float> values = StaticData::Instance().GetAllWeights().GetScoresForProducer(ff);
cout << ff->GetScoreProducerDescription() << "=";
for (size_t i = 0; i < numScoreComps; ++i) {
cout << " " << values[i];
}
cout << endl;
}
else {
if (ff->GetSparseProducerWeight() == 1)
cout << ff->GetScoreProducerDescription() << "= sparse" << endl;
else
cout << ff->GetScoreProducerDescription() << "= " << ff->GetSparseProducerWeight() << endl;
vector<float> values = StaticData::Instance().GetAllWeights().GetScoresForProducer(ff);
for (size_t i = 0; i < numScoreComps; ++i) {
cout << " " << values[i];
}
cout << endl;
}
static void ShowWeights()

2
moses/FeatureFunction.h
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@ -109,8 +109,6 @@ public:
const std::string& GetScoreProducerDescription() const
{ return m_description; }
virtual float GetSparseProducerWeight() const { return 1; }
virtual bool IsTuneable() const { return m_tuneable; }
//!

4
moses/GlobalLexicalModelUnlimited.h
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@ -60,7 +60,6 @@ private:
bool m_bitrigger;
bool m_biasFeature;
float m_sparseProducerWeight;
bool m_ignorePunctuation;
std::set<std::string> m_vocabSource;
@ -93,9 +92,6 @@ public:
, ScoreComponentCollection &scoreBreakdown
, ScoreComponentCollection &estimatedFutureScore) const;
void SetSparseProducerWeight(float weight) { m_sparseProducerWeight = weight; }
float GetSparseProducerWeight() const { return m_sparseProducerWeight; }
void AddFeature(ScoreComponentCollection* accumulator,
StringPiece sourceTrigger, StringPiece sourceWord, StringPiece targetTrigger,
StringPiece targetWord) const;

7
moses/Hypothesis.cpp
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@ -308,13 +308,6 @@ void Hypothesis::CalcScore(const SquareMatrix &futureScore)
// FUTURE COST
m_futureScore = futureScore.CalcFutureScore( m_sourceCompleted );
// Apply sparse producer weights
const vector<const FeatureFunction*>& sparseProducers = StaticData::Instance().GetSparseProducers();
for (unsigned i = 0; i < sparseProducers.size(); ++i) {
float weight = sparseProducers[i]->GetSparseProducerWeight();
m_scoreBreakdown.MultiplyEquals(sparseProducers[i], weight);
}
// TOTAL
m_totalScore = m_scoreBreakdown.GetWeightedScore() + m_futureScore;

4
moses/PhraseBoundaryFeature.h
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@ -49,9 +49,6 @@ public:
virtual void Evaluate(const TargetPhrase &targetPhrase
, ScoreComponentCollection &scoreBreakdown
, ScoreComponentCollection &estimatedFutureScore) const;
void SetSparseProducerWeight(float weight) { m_sparseProducerWeight = weight; }
float GetSparseProducerWeight() const { return m_sparseProducerWeight; }
private:
void AddFeatures(
@ -59,7 +56,6 @@ private:
const std::string& side, ScoreComponentCollection* scores) const ;
FactorList m_sourceFactors;
FactorList m_targetFactors;
float m_sparseProducerWeight;
};
}

1
moses/PhrasePairFeature.cpp
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@ -26,7 +26,6 @@ PhrasePairFeature::PhrasePairFeature(const std::string &line)
m_simple = Scan<bool>(tokens[4]);
m_sourceContext = Scan<bool>(tokens[5]);
m_domainTrigger = Scan<bool>(tokens[6]);
m_sparseProducerWeight = 1;
m_ignorePunctuation = Scan<bool>(tokens[6]);
if (m_simple == 1) std::cerr << "using simple phrase pairs.. ";

3
moses/PhrasePairFeature.h
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@ -26,7 +26,6 @@ class PhrasePairFeature: public StatelessFeatureFunction {
bool m_simple;
bool m_sourceContext;
bool m_domainTrigger;
float m_sparseProducerWeight;
bool m_ignorePunctuation;
CharHash m_punctuationHash;
@ -47,8 +46,6 @@ class PhrasePairFeature: public StatelessFeatureFunction {
bool Load(const std::string &filePathSource/*, const std::string &filePathTarget*/);
void SetSparseProducerWeight(float weight) { m_sparseProducerWeight = weight; }
float GetSparseProducerWeight() const { return m_sparseProducerWeight; }
};
}

9
moses/StaticData.h
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@ -662,15 +662,6 @@ public:
return m_nBestIncludesSegmentation;
}
const std::vector<const FeatureFunction*>& GetSparseProducers() const {return m_sparseProducers;}
void AddSparseProducer(const FeatureFunction* ff) {
m_sparseProducers.push_back(ff);
}
//All sparse producers that have an activated global weight
std::vector<const FeatureFunction*> m_sparseProducers;
float GetWeightWordPenalty() const;
float GetWeightUnknownWordPenalty() const;

1
moses/TargetNgramFeature.cpp
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@ -39,7 +39,6 @@ int TargetNgramState::Compare(const FFState& other) const {
TargetNgramFeature::TargetNgramFeature(const std::string &line)
:StatefulFeatureFunction("TargetNgramFeature", 0, line)
,m_sparseProducerWeight(1)
{
std::cerr << "Initializing target ngram feature.." << std::endl;

6
moses/TargetNgramFeature.h
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@ -184,9 +184,6 @@ public:
bool Load(const std::string &filePath);
void SetSparseProducerWeight(float weight) { m_sparseProducerWeight = weight; }
float GetSparseProducerWeight() const { return m_sparseProducerWeight; }
virtual const FFState* EmptyHypothesisState(const InputType &input) const;
virtual FFState* Evaluate(const Hypothesis& cur_hypo, const FFState* prev_state,
@ -206,9 +203,6 @@ private:
size_t m_n;
bool m_lower_ngrams;
// additional weight that all sparse weights are scaled with
float m_sparseProducerWeight;
std::string m_baseName;
void appendNgram(const Word& word, bool& skip, std::stringstream& ngram) const;

1
moses/WordTranslationFeature.cpp
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@ -17,7 +17,6 @@ using namespace std;
WordTranslationFeature::WordTranslationFeature(const std::string &line)
:StatelessFeatureFunction("WordTranslationFeature", 0, line)
,m_unrestricted(true)
,m_sparseProducerWeight(1)
,m_simple(true)
,m_sourceContext(false)
,m_targetContext(false)

4
moses/WordTranslationFeature.h
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@ -31,7 +31,6 @@ private:
bool m_sourceContext;
bool m_targetContext;
bool m_domainTrigger;
float m_sparseProducerWeight;
bool m_ignorePunctuation;
CharHash m_punctuationHash;
@ -54,9 +53,6 @@ public:
, ScoreComponentCollection &scoreBreakdown
, ScoreComponentCollection &estimatedFutureScore) const;
void SetSparseProducerWeight(float weight) { m_sparseProducerWeight = weight; }
float GetSparseProducerWeight() const { return m_sparseProducerWeight; }
StatelessFeatureType GetStatelessFeatureType() const
{ return RequiresSegmentation; }
};