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//TODO: non-sparse vector for all feature functions? modelset applymodels keeps track of who has what features? it's nice having FF that could generate a handful out of 10000 possible feats, though.
//TODO: actually score rule_feature()==true features once only, hash keyed on rule or modify TRule directly? need to keep clear in forest which features come from models vs. rules; then rescoring could drop all the old models features at once
#include <boost/lexical_cast.hpp>
#include <stdexcept>
#include "ff.h"
#include "tdict.h"
#include "hg.h"
using namespace std;
FeatureFunction::~FeatureFunction() {}
void FeatureFunction::FinalTraversalFeatures(const void* /* ant_state */,
SparseVector<double>* /* features */) const {
}
string FeatureFunction::usage_helper(std::string const& name,std::string const& params,std::string const& details,bool sp,bool sd) {
string r=name;
if (sp) {
r+=": ";
r+=params;
}
if (sd) {
r+="\n";
r+=details;
}
return r;
}
Features FeatureFunction::single_feature(WordID feat) {
return Features(1,feat);
}
Features ModelSet::all_features(std::ostream *warn,bool warn0) {
typedef Features FFS;
FFS ffs;
#define WARNFF(x) do { if (warn) { *warn << "WARNING: "<< x ; *warn<<endl; } } while(0)
typedef std::map<WordID,string> FFM;
FFM ff_from;
for (unsigned i=0;i<models_.size();++i) {
FeatureFunction const& ff=*models_[i];
string const& ffname=ff.name;
FFS si=ff.features();
if (si.empty()) {
WARNFF(ffname<<" doesn't yet report any feature IDs - implement features() method?");
}
unsigned n0=0;
for (unsigned j=0;j<si.size();++j) {
WordID fid=si[j];
if (!fid) ++n0;
if (fid >= weights_.size())
weights_.resize(fid+1);
if (warn0 || fid) {
pair<FFM::iterator,bool> i_new=ff_from.insert(FFM::value_type(fid,ffname));
if (i_new.second) {
if (fid)
ffs.push_back(fid);
else
WARNFF("Feature id 0 for "<<ffname<<" (models["<<i<<"]) - probably no weight provided. Don't freeze feature ids to see the name");
} else {
WARNFF(ffname<<" (models["<<i<<"]) tried to define feature "<<FD::Convert(fid)<<" already defined earlier by "<<i_new.first->second);
}
}
}
if (n0)
WARNFF(ffname<<" (models["<<i<<"]) had "<<n0<<" unused features (--no_freeze_feature_set to see them)");
}
return ffs;
#undef WARNFF
}
void ModelSet::show_features(std::ostream &out,std::ostream &warn,bool warn_zero_wt)
{
typedef Features FFS;
FFS ffs=all_features(&warn,warn_zero_wt);
out << "Weight Feature\n";
for (unsigned i=0;i<ffs.size();++i) {
WordID fid=ffs[i];
string const& fname=FD::Convert(fid);
double wt=weights_[fid];
if (warn_zero_wt && wt==0)
warn<<"WARNING: "<<fname<<" has 0 weight."<<endl;
out << wt << " " << fname<<endl;
}
}
// Hiero and Joshua use log_10(e) as the value, so I do to
WordPenalty::WordPenalty(const string& param) :
fid_(FD::Convert("WordPenalty")),
value_(-1.0 / log(10)) {
if (!param.empty()) {
cerr << "Warning WordPenalty ignoring parameter: " << param << endl;
}
}
void FeatureFunction::TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const std::vector<const void*>& ant_states,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* state) const {
throw std::runtime_error("TraversalFeaturesImpl not implemented - override it or TraversalFeaturesLog.\n");
abort();
}
void WordPenalty::TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const std::vector<const void*>& ant_states,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* state) const {
(void) smeta;
(void) ant_states;
(void) state;
(void) estimated_features;
features->set_value(fid_, edge.rule_->EWords() * value_);
}
SourceWordPenalty::SourceWordPenalty(const string& param) :
fid_(FD::Convert("SourceWordPenalty")),
value_(-1.0 / log(10)) {
if (!param.empty()) {
cerr << "Warning SourceWordPenalty ignoring parameter: " << param << endl;
}
}
Features SourceWordPenalty::features() const {
return single_feature(fid_);
}
Features WordPenalty::features() const {
return single_feature(fid_);
}
void SourceWordPenalty::TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const std::vector<const void*>& ant_states,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* state) const {
(void) smeta;
(void) ant_states;
(void) state;
(void) estimated_features;
features->set_value(fid_, edge.rule_->FWords() * value_);
}
ArityPenalty::ArityPenalty(const std::string& param) :
value_(-1.0 / log(10)) {
string fname = "Arity_";
unsigned MAX=DEFAULT_MAX_ARITY;
using namespace boost;
if (!param.empty())
MAX=lexical_cast<unsigned>(param);
for (unsigned i = 0; i <= MAX; ++i) {
WordID fid=FD::Convert(fname+lexical_cast<string>(i));
fids_.push_back(fid);
}
while (!fids_.empty() && fids_.back()==0) fids_.pop_back(); // pretty up features vector in case FD was frozen. doesn't change anything
}
Features ArityPenalty::features() const {
return Features(fids_.begin(),fids_.end());
}
void ArityPenalty::TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const std::vector<const void*>& ant_states,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* state) const {
(void) smeta;
(void) ant_states;
(void) state;
(void) estimated_features;
unsigned a=edge.Arity();
features->set_value(a<fids_.size()?fids_[a]:0, value_);
}
ModelSet::ModelSet(const vector<double>& w, const vector<const FeatureFunction*>& models) :
models_(models),
weights_(w),
state_size_(0),
model_state_pos_(models.size()) {
for (int i = 0; i < models_.size(); ++i) {
model_state_pos_[i] = state_size_;
state_size_ += models_[i]->NumBytesContext();
}
}
void ModelSet::AddFeaturesToEdge(const SentenceMetadata& smeta,
const Hypergraph& /* hg */,
const vector<string>& node_states,
Hypergraph::Edge* edge,
string* context,
prob_t* combination_cost_estimate) const {
edge->reset_info();
context->resize(state_size_);
memset(&(*context)[0], 0, state_size_); //FIXME: only context.data() is required to be contiguous, and it becomes invalid after next string operation. use SmallVector<char>? ValueArray? (higher performance perhaps, fixed size)
SparseVector<double> est_vals; // only computed if combination_cost_estimate is non-NULL
if (combination_cost_estimate) *combination_cost_estimate = prob_t::One();
for (int i = 0; i < models_.size(); ++i) {
const FeatureFunction& ff = *models_[i];
void* cur_ff_context = NULL;
vector<const void*> ants(edge->tail_nodes_.size());
bool has_context = ff.NumBytesContext() > 0;
if (has_context) {
int spos = model_state_pos_[i];
cur_ff_context = &(*context)[spos];
for (int i = 0; i < ants.size(); ++i) {
ants[i] = &node_states[edge->tail_nodes_[i]][spos];
}
}
ff.TraversalFeatures(smeta, *edge, ants, &edge->feature_values_, &est_vals, cur_ff_context);
}
if (combination_cost_estimate)
combination_cost_estimate->logeq(est_vals.dot(weights_));
edge->edge_prob_.logeq(edge->feature_values_.dot(weights_));
}
void ModelSet::AddFinalFeatures(const std::string& state, Hypergraph::Edge* edge,SentenceMetadata const& smeta) const {
assert(1 == edge->rule_->Arity());
edge->reset_info();
for (int i = 0; i < models_.size(); ++i) {
const FeatureFunction& ff = *models_[i];
const void* ant_state = NULL;
bool has_context = ff.NumBytesContext() > 0;
if (has_context) {
int spos = model_state_pos_[i];
ant_state = &state[spos];
}
ff.FinalTraversalFeatures(smeta, *edge, ant_state, &edge->feature_values_);
}
edge->edge_prob_.logeq(edge->feature_values_.dot(weights_));
}
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