mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-12-27 22:14:57 +03:00
447 lines
14 KiB
C++
447 lines
14 KiB
C++
// $Id$
|
|
|
|
/***********************************************************************
|
|
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
|
|
***********************************************************************/
|
|
|
|
#ifndef moses_ScoreComponentCollection_h
|
|
#define moses_ScoreComponentCollection_h
|
|
|
|
#include <numeric>
|
|
#include <sstream>
|
|
|
|
#ifdef MPI_ENABLE
|
|
#include <boost/serialization/access.hpp>
|
|
#include <boost/serialization/split_member.hpp>
|
|
#endif
|
|
|
|
#include "util/check.hh"
|
|
|
|
#include "LMList.h"
|
|
#include "ScoreProducer.h"
|
|
#include "FeatureVector.h"
|
|
#include "TypeDef.h"
|
|
#include "Util.h"
|
|
|
|
|
|
namespace Moses
|
|
{
|
|
|
|
|
|
/*** An unweighted collection of scores for a translation or step in a translation.
|
|
*
|
|
* In the factored phrase-based models that are implemented by moses, there are a set of
|
|
* scores that come from a variety of sources (translation probabilities, language model
|
|
* probablilities, distortion probabilities, generation probabilities). Furthermore, while
|
|
* some of these scores may be 0, this number is fixed (and generally quite small, ie, less
|
|
* than 15), for a given model.
|
|
*
|
|
* The values contained in ScoreComponentCollection objects are unweighted scores (log-probs).
|
|
*
|
|
* ScoreComponentCollection objects can be added and subtracted, which makes them appropriate
|
|
* to be the datatype used to return the result of a score computations (in this case they will
|
|
* have most values set to zero, except for the ones that are results of the indivudal computation
|
|
* this will then be added into the "running total" in the Hypothesis. In fact, for a score
|
|
* to be tracked in the hypothesis (and thus to participate in the decoding process), a class
|
|
* representing that score must extend the ScoreProducer abstract base class. For an example
|
|
* refer to the DistortionScoreProducer class.
|
|
*/
|
|
class ScoreComponentCollection
|
|
{
|
|
friend std::ostream& operator<<(std::ostream& os, const ScoreComponentCollection& rhs);
|
|
private:
|
|
FVector m_scores;
|
|
typedef std::pair<size_t,size_t> IndexPair;
|
|
typedef std::map<const ScoreProducer*,IndexPair> ScoreIndexMap;
|
|
static ScoreIndexMap s_scoreIndexes;
|
|
static size_t s_denseVectorSize;
|
|
static IndexPair GetIndexes(const ScoreProducer* sp)
|
|
{
|
|
ScoreIndexMap::const_iterator indexIter = s_scoreIndexes.find(sp);
|
|
if (indexIter == s_scoreIndexes.end()) {
|
|
std::cerr << "ERROR: ScoreProducer: " << sp->GetScoreProducerDescription() <<
|
|
" not registered with ScoreIndexMap" << std::endl;
|
|
std::cerr << "You must call ScoreComponentCollection.RegisterScoreProducer() " <<
|
|
" for every ScoreProducer" << std::endl;
|
|
abort();
|
|
}
|
|
return indexIter->second;
|
|
}
|
|
|
|
public:
|
|
static void ResetCounter() {
|
|
s_denseVectorSize = 0;
|
|
}
|
|
|
|
//! Create a new score collection with all values set to 0.0
|
|
ScoreComponentCollection();
|
|
|
|
//! Clone a score collection
|
|
ScoreComponentCollection(const ScoreComponentCollection& rhs)
|
|
: m_scores(rhs.m_scores)
|
|
{}
|
|
|
|
ScoreComponentCollection& operator=( const ScoreComponentCollection& rhs ) {
|
|
m_scores = rhs.m_scores;
|
|
return *this;
|
|
}
|
|
|
|
/**
|
|
* Register a ScoreProducer with a fixed number of scores, so that it can
|
|
* be allocated space in the dense part of the feature vector.
|
|
**/
|
|
static void RegisterScoreProducer(const ScoreProducer* scoreProducer);
|
|
static void UnregisterScoreProducer(const ScoreProducer* scoreProducer);
|
|
|
|
/** Load from file */
|
|
bool Load(const std::string& filename)
|
|
{
|
|
return m_scores.load(filename);
|
|
}
|
|
|
|
const FVector& GetScoresVector() const
|
|
{
|
|
return m_scores;
|
|
}
|
|
|
|
const std::valarray<FValue> &getCoreFeatures() const {
|
|
return m_scores.getCoreFeatures();
|
|
}
|
|
|
|
size_t Size() const
|
|
{
|
|
return m_scores.size();
|
|
}
|
|
|
|
void Resize()
|
|
{
|
|
if (m_scores.coreSize() != s_denseVectorSize) {
|
|
m_scores.resize(s_denseVectorSize);
|
|
}
|
|
}
|
|
|
|
/** Create and FVector with the right number of core features */
|
|
static FVector CreateFVector()
|
|
{
|
|
return FVector(s_denseVectorSize);
|
|
}
|
|
|
|
void SetToBinaryOf(const ScoreComponentCollection& rhs)
|
|
{
|
|
m_scores.setToBinaryOf(rhs.m_scores);
|
|
}
|
|
|
|
//! Set all values to 0.0
|
|
void ZeroAll()
|
|
{
|
|
m_scores.clear();
|
|
}
|
|
|
|
void MultiplyEquals(float scalar);
|
|
void DivideEquals(float scalar);
|
|
void CoreDivideEquals(float scalar);
|
|
void DivideEquals(const ScoreComponentCollection& rhs);
|
|
void MultiplyEquals(const ScoreComponentCollection& rhs);
|
|
void MultiplyEqualsBackoff(const ScoreComponentCollection& rhs, float backoff);
|
|
void MultiplyEquals(float core_r0, float sparse_r0);
|
|
void MultiplyEquals(const ScoreProducer* sp, float scalar);
|
|
|
|
size_t GetNumberWeights(const ScoreProducer* sp);
|
|
|
|
void CoreAssign(const ScoreComponentCollection& rhs)
|
|
{
|
|
m_scores.coreAssign(rhs.m_scores);
|
|
}
|
|
|
|
//! add the score in rhs
|
|
void PlusEquals(const ScoreComponentCollection& rhs)
|
|
{
|
|
m_scores += rhs.m_scores;
|
|
}
|
|
|
|
// add only sparse features
|
|
void SparsePlusEquals(const ScoreComponentCollection& rhs)
|
|
{
|
|
m_scores.sparsePlusEquals(rhs.m_scores);
|
|
}
|
|
|
|
void PlusEquals(const FVector& scores)
|
|
{
|
|
m_scores += scores;
|
|
}
|
|
|
|
//! subtract the score in rhs
|
|
void MinusEquals(const ScoreComponentCollection& rhs)
|
|
{
|
|
m_scores -= rhs.m_scores;
|
|
}
|
|
|
|
//For features which have an unbounded number of components
|
|
void MinusEquals(const ScoreProducer*sp, const std::string& name, float score)
|
|
{
|
|
assert(sp->GetNumScoreComponents() == ScoreProducer::unlimited);
|
|
FName fname(sp->GetScoreProducerDescription(),name);
|
|
m_scores[fname] -= score;
|
|
}
|
|
|
|
//For features which have an unbounded number of components
|
|
void SparseMinusEquals(const std::string& full_name, float score)
|
|
{
|
|
FName fname(full_name);
|
|
m_scores[fname] -= score;
|
|
}
|
|
|
|
//! Add scores from a single ScoreProducer only
|
|
//! The length of scores must be equal to the number of score components
|
|
//! produced by sp
|
|
void PlusEquals(const ScoreProducer* sp, const ScoreComponentCollection& scores)
|
|
{
|
|
IndexPair indexes = GetIndexes(sp);
|
|
for (size_t i = indexes.first; i < indexes.second; ++i) {
|
|
m_scores[i] += scores.m_scores[i];
|
|
}
|
|
}
|
|
|
|
//! Add scores from a single ScoreProducer only
|
|
//! The length of scores must be equal to the number of score components
|
|
//! produced by sp
|
|
void PlusEquals(const ScoreProducer* sp, const std::vector<float>& scores)
|
|
{
|
|
IndexPair indexes = GetIndexes(sp);
|
|
CHECK(scores.size() == indexes.second - indexes.first);
|
|
for (size_t i = 0; i < scores.size(); ++i) {
|
|
m_scores[i + indexes.first] += scores[i];
|
|
}
|
|
}
|
|
|
|
//! Special version PlusEquals(ScoreProducer, vector<float>)
|
|
//! to add the score from a single ScoreProducer that produces
|
|
//! a single value
|
|
void PlusEquals(const ScoreProducer* sp, float score)
|
|
{
|
|
IndexPair indexes = GetIndexes(sp);
|
|
CHECK(1 == indexes.second - indexes.first);
|
|
m_scores[indexes.first] += score;
|
|
}
|
|
|
|
//For features which have an unbounded number of components
|
|
void PlusEquals(const ScoreProducer*sp, const std::string& name, float score)
|
|
{
|
|
CHECK(sp->GetNumScoreComponents() == ScoreProducer::unlimited);
|
|
FName fname(sp->GetScoreProducerDescription(),name);
|
|
m_scores[fname] += score;
|
|
}
|
|
|
|
//For features which have an unbounded number of components
|
|
void SparsePlusEquals(const std::string& full_name, float score)
|
|
{
|
|
FName fname(full_name);
|
|
m_scores[fname] += score;
|
|
}
|
|
|
|
void Assign(const ScoreProducer* sp, const std::vector<float>& scores)
|
|
{
|
|
IndexPair indexes = GetIndexes(sp);
|
|
CHECK(scores.size() == indexes.second - indexes.first);
|
|
for (size_t i = 0; i < scores.size(); ++i) {
|
|
m_scores[i + indexes.first] = scores[i];
|
|
}
|
|
}
|
|
|
|
//! Special version Assign(ScoreProducer, vector<float>)
|
|
//! to add the score from a single ScoreProducer that produces
|
|
//! a single value
|
|
void Assign(const ScoreProducer* sp, float score)
|
|
{
|
|
IndexPair indexes = GetIndexes(sp);
|
|
CHECK(1 == indexes.second - indexes.first);
|
|
m_scores[indexes.first] = score;
|
|
}
|
|
|
|
// Assign core weight by index
|
|
void Assign(size_t index, float score) {
|
|
m_scores[index] = score;
|
|
}
|
|
|
|
//For features which have an unbounded number of components
|
|
void Assign(const ScoreProducer*sp, const std::string name, float score)
|
|
{
|
|
CHECK(sp->GetNumScoreComponents() == ScoreProducer::unlimited);
|
|
FName fname(sp->GetScoreProducerDescription(),name);
|
|
m_scores[fname] = score;
|
|
}
|
|
|
|
|
|
//Read sparse features from string
|
|
void Assign(const ScoreProducer* sp, const std::string line);
|
|
|
|
// shortcut: setting the value directly using the feature name
|
|
void Assign(const std::string name, float score)
|
|
{
|
|
FName fname(name);
|
|
m_scores[fname] = score;
|
|
}
|
|
|
|
float InnerProduct(const ScoreComponentCollection& rhs) const
|
|
{
|
|
return m_scores.inner_product(rhs.m_scores);
|
|
}
|
|
|
|
float PartialInnerProduct(const ScoreProducer* sp, const std::vector<float>& rhs) const
|
|
{
|
|
std::vector<float> lhs = GetScoresForProducer(sp);
|
|
CHECK(lhs.size() == rhs.size());
|
|
return std::inner_product(lhs.begin(), lhs.end(), rhs.begin(), 0.0f);
|
|
}
|
|
|
|
//! return a vector of all the scores associated with a certain ScoreProducer
|
|
std::vector<float> GetScoresForProducer(const ScoreProducer* sp) const
|
|
{
|
|
size_t components = sp->GetNumScoreComponents();
|
|
if (components == ScoreProducer::unlimited) return std::vector<float>();
|
|
std::vector<float> res(components);
|
|
IndexPair indexes = GetIndexes(sp);
|
|
for (size_t i = 0; i < res.size(); ++i) {
|
|
res[i] = m_scores[i + indexes.first];
|
|
}
|
|
return res;
|
|
}
|
|
|
|
//! get subset of scores that belong to a certain sparse ScoreProducer
|
|
FVector GetVectorForProducer(const ScoreProducer* sp) const
|
|
{
|
|
FVector fv(s_denseVectorSize);
|
|
std::string prefix = sp->GetScoreProducerDescription() + FName::SEP;
|
|
for(FVector::FNVmap::const_iterator i = m_scores.cbegin(); i != m_scores.cend(); i++) {
|
|
std::stringstream name;
|
|
name << i->first;
|
|
if (name.str().substr( 0, prefix.length() ).compare( prefix ) == 0)
|
|
fv[i->first] = i->second;
|
|
}
|
|
return fv;
|
|
}
|
|
|
|
float GetSparseWeight(const FName& featureName) const
|
|
{
|
|
return m_scores[featureName];
|
|
}
|
|
|
|
void PrintCoreFeatures() {
|
|
m_scores.printCoreFeatures();
|
|
}
|
|
|
|
void ThresholdScaling(float maxValue)
|
|
{
|
|
// find (smallest) factor for which all weights are <= maxValue
|
|
// 0.1 / 0.14 = 0.714285714
|
|
// 0.1 / 0.17 = 0.588235294
|
|
m_scores.thresholdScale(maxValue);
|
|
}
|
|
|
|
void CapMax(float maxValue)
|
|
{
|
|
// cap all sparse features to maxValue
|
|
m_scores.capMax(maxValue);
|
|
}
|
|
|
|
void CapMin(float minValue)
|
|
{
|
|
// cap all sparse features to minValue
|
|
m_scores.capMin(minValue);
|
|
}
|
|
|
|
//! if a ScoreProducer produces a single score (for example, a language model score)
|
|
//! this will return it. If not, this method will throw
|
|
float GetScoreForProducer(const ScoreProducer* sp) const
|
|
{
|
|
IndexPair indexes = GetIndexes(sp);
|
|
CHECK(indexes.second - indexes.first == 1);
|
|
return m_scores[indexes.first];
|
|
}
|
|
|
|
//For features which have an unbounded number of components
|
|
float GetScoreForProducer
|
|
(const ScoreProducer* sp, const std::string& name) const
|
|
{
|
|
CHECK(sp->GetNumScoreComponents() == ScoreProducer::unlimited);
|
|
FName fname(sp->GetScoreProducerDescription(),name);
|
|
return m_scores[fname];
|
|
}
|
|
|
|
float GetWeightedScore() const;
|
|
|
|
void ZeroAllLM(const LMList& lmList);
|
|
void PlusEqualsAllLM(const LMList& lmList, const ScoreComponentCollection& rhs);
|
|
void L1Normalise();
|
|
float GetL1Norm() const;
|
|
float GetL2Norm() const;
|
|
float GetLInfNorm() const;
|
|
size_t L1Regularize(float lambda);
|
|
void L2Regularize(float lambda);
|
|
size_t SparseL1Regularize(float lambda);
|
|
void SparseL2Regularize(float lambda);
|
|
void Save(const std::string& filename) const;
|
|
void Save(std::ostream&) const;
|
|
|
|
void IncrementSparseHopeFeatures() { m_scores.incrementSparseHopeFeatures(); }
|
|
void IncrementSparseFearFeatures() { m_scores.incrementSparseFearFeatures(); }
|
|
void PrintSparseHopeFeatureCounts(std::ofstream& out) { m_scores.printSparseHopeFeatureCounts(out); }
|
|
void PrintSparseFearFeatureCounts(std::ofstream& out) { m_scores.printSparseFearFeatureCounts(out); }
|
|
void PrintSparseHopeFeatureCounts() { m_scores.printSparseHopeFeatureCounts(); }
|
|
void PrintSparseFearFeatureCounts() { m_scores.printSparseFearFeatureCounts(); }
|
|
size_t PruneSparseFeatures(size_t threshold) { return m_scores.pruneSparseFeatures(threshold); }
|
|
size_t PruneZeroWeightFeatures() { return m_scores.pruneZeroWeightFeatures(); }
|
|
void UpdateConfidenceCounts(ScoreComponentCollection &weightUpdate, bool signedCounts) { m_scores.updateConfidenceCounts(weightUpdate.m_scores, signedCounts); }
|
|
void UpdateLearningRates(float decay_core, float decay_sparse, ScoreComponentCollection &confidenceCounts, float core_r0, float sparse_r0) { m_scores.updateLearningRates(decay_core, decay_sparse, confidenceCounts.m_scores, core_r0, sparse_r0); }
|
|
|
|
#ifdef MPI_ENABLE
|
|
public:
|
|
friend class boost::serialization::access;
|
|
|
|
private:
|
|
//serialization
|
|
template<class Archive>
|
|
void save(Archive &ar, const unsigned int version) const {
|
|
ar << m_scores;
|
|
}
|
|
|
|
template<class Archive>
|
|
void load(Archive &ar, const unsigned int version) {
|
|
ar >> m_scores;
|
|
|
|
}
|
|
|
|
BOOST_SERIALIZATION_SPLIT_MEMBER()
|
|
|
|
#endif
|
|
|
|
};
|
|
|
|
struct SCCPlus {
|
|
ScoreComponentCollection operator()
|
|
(const ScoreComponentCollection& lhs,
|
|
const ScoreComponentCollection& rhs) {
|
|
ScoreComponentCollection sum(lhs);
|
|
sum.PlusEquals(rhs);
|
|
return sum;
|
|
}
|
|
};
|
|
|
|
}
|
|
#endif
|