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-rw-r--r--decoder/cdec.cc12
-rw-r--r--decoder/ff.cc42
-rw-r--r--decoder/ff.h22
3 files changed, 50 insertions, 26 deletions
diff --git a/decoder/cdec.cc b/decoder/cdec.cc
index 079b270b..79d51939 100644
--- a/decoder/cdec.cc
+++ b/decoder/cdec.cc
@@ -390,6 +390,10 @@ int main(int argc, char** argv) {
}
// cerr << "+LM weights: " << FeatureVector(feature_weights)<<endl;
}
+ if (!conf.count("no_freeze_feature_set")) {
+ cerr << "Freezing feature set (use --no_freeze_feature_set to change)." << endl;
+ FD::Freeze(); // this means we can't see the feature names of not-weighted features
+ }
// set up translation back end
if (formalism == "scfg")
@@ -443,10 +447,6 @@ int main(int argc, char** argv) {
ModelSet prelm_models(prelm_feature_weights, prelm_ffs);
if (has_prelm_models)
show_models(conf,prelm_models,"prelm ");
- if (!conf.count("no_freeze_feature_set")) { // this used to happen immediately after loading weights, but now show_models will extend weight vector nicely.
- cerr << "Freezing feature set (use --no_freeze_feature_set to change)." << endl;
- FD::Freeze();
- }
int palg = 1;
if (LowercaseString(str("intersection_strategy",conf)) == "full") {
@@ -518,6 +518,7 @@ int main(int argc, char** argv) {
Timer t("Translation");
const bool translation_successful =
translator->Translate(to_translate, &smeta, feature_weights, &forest);
+ //TODO: modify translator to incorporate all 0-state model scores immediately?
translator->SentenceComplete();
if (!translation_successful) {
cerr << " NO PARSE FOUND.\n";
@@ -550,8 +551,7 @@ int main(int argc, char** argv) {
ApplyModelSet(forest,
smeta,
prelm_models,
- IntersectionConfiguration(exhaustive_t()),
-// avoid overhead of best-first
+ inter_conf, // this is now reduced to exhaustive if all are stateless
&prelm_forest);
forest.swap(prelm_forest);
forest.Reweight(prelm_feature_weights);
diff --git a/decoder/ff.cc b/decoder/ff.cc
index 3f433dfb..b323ab27 100644
--- a/decoder/ff.cc
+++ b/decoder/ff.cc
@@ -1,5 +1,6 @@
//TODO: 0 size state != rule-local feature, i.e. still may depend on source span loc/context. identify truly rule-local features so if we want they can be added to grammar rules (minor speedup)
+#include <boost/lexical_cast.hpp>
#include "ff.h"
#include "tdict.h"
@@ -33,7 +34,7 @@ FeatureFunction::Features FeatureFunction::single_feature(WordID feat) {
return Features(1,feat);
}
-FeatureFunction::Features ModelSet::all_features(std::ostream *warn) {
+FeatureFunction::Features ModelSet::all_features(std::ostream *warn,bool warn0) {
typedef FeatureFunction::Features FFS;
FFS ffs;
#define WARNFF(x) do { if (warn) { *warn << "WARNING: "<< x ; *warn<<endl; } } while(0)
@@ -46,17 +47,26 @@ FeatureFunction::Features ModelSet::all_features(std::ostream *warn) {
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);
- pair<FFM::iterator,bool> i_new=ff_from.insert(FFM::value_type(fid,ffname));
- if (i_new.second)
- ffs.push_back(fid);
- else {
- WARNFF(ffname<<" models["<<i<<"] tried to define feature "<<FD::Convert(fid)<<" already defined earlier by "<<i_new.first->second);
+ 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
@@ -130,17 +140,22 @@ void SourceWordPenalty::TraversalFeaturesImpl(const SentenceMetadata& smeta,
features->set_value(fid_, edge.rule_->FWords() * value_);
}
-ArityPenalty::ArityPenalty(const std::string& /* param */) :
+ArityPenalty::ArityPenalty(const std::string& param) :
value_(-1.0 / log(10)) {
- string fname = "Arity_X";
- for (int i = 0; i < N_ARITIES; ++i) {
- fname[6]=i + '0';
- fids_[i] = FD::Convert(fname);
+ 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
}
FeatureFunction::Features ArityPenalty::features() const {
- return Features(&fids_[0],&fids_[N_ARITIES]);
+ return Features(fids_.begin(),fids_.end());
}
void ArityPenalty::TraversalFeaturesImpl(const SentenceMetadata& smeta,
@@ -153,7 +168,8 @@ void ArityPenalty::TraversalFeaturesImpl(const SentenceMetadata& smeta,
(void) ant_states;
(void) state;
(void) estimated_features;
- features->set_value(fids_[edge.Arity()], value_);
+ 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) :
diff --git a/decoder/ff.h b/decoder/ff.h
index 6f8b8626..2cf96d39 100644
--- a/decoder/ff.h
+++ b/decoder/ff.h
@@ -2,7 +2,6 @@
#define _FF_H_
#include <vector>
-
#include "fdict.h"
#include "hg.h"
@@ -31,7 +30,10 @@ protected:
static std::string usage_helper(std::string const& name,std::string const& params,std::string const& details,bool show_params,bool show_details);
static Features single_feature(WordID feat);
public:
+ // stateless feature that doesn't depend on source span: override and return true. then your feature can be precomputed over rules.
+ virtual bool rule_feature() const { return false; }
+ //OVERRIDE THIS:
virtual Features features() const { return Features(); }
// returns the number of bytes of context that this feature function will
// (maximally) use. By default, 0 ("stateless" models in Hiero/Joshua).
@@ -95,6 +97,7 @@ class WordPenalty : public FeatureFunction {
static std::string usage(bool p,bool d) {
return usage_helper("WordPenalty","","number of target words (local feature)",p,d);
}
+ bool rule_feature() const { return true; }
protected:
virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
@@ -109,6 +112,7 @@ class WordPenalty : public FeatureFunction {
class SourceWordPenalty : public FeatureFunction {
public:
+ bool rule_feature() const { return true; }
Features features() const;
SourceWordPenalty(const std::string& param);
static std::string usage(bool p,bool d) {
@@ -126,12 +130,18 @@ class SourceWordPenalty : public FeatureFunction {
const double value_;
};
+#define DEFAULT_MAX_ARITY 9
+#define DEFAULT_MAX_ARITY_STRINGIZE(x) #x
+#define DEFAULT_MAX_ARITY_STRINGIZE_EVAL(x) DEFAULT_MAX_ARITY_STRINGIZE(x)
+#define DEFAULT_MAX_ARITY_STR DEFAULT_MAX_ARITY_STRINGIZE_EVAL(DEFAULT_MAX_ARITY)
+
class ArityPenalty : public FeatureFunction {
public:
+ bool rule_feature() const { return true; }
Features features() const;
ArityPenalty(const std::string& param);
static std::string usage(bool p,bool d) {
- return usage_helper("ArityPenalty","","Indicator feature Arity_N=1 for rule of arity N (local feature)",p,d);
+ return usage_helper("ArityPenalty","[MaxArity(default " DEFAULT_MAX_ARITY_STR ")]","Indicator feature Arity_N=1 for rule of arity N (local feature). 0<=N<=MaxArity(default " DEFAULT_MAX_ARITY_STR ")",p,d);
}
protected:
@@ -142,10 +152,7 @@ class ArityPenalty : public FeatureFunction {
SparseVector<double>* estimated_features,
void* context) const;
private:
- enum {N_ARITIES=10};
-
-
- int fids_[N_ARITIES];
+ std::vector<WordID> fids_;
const double value_;
};
@@ -173,7 +180,8 @@ class ModelSet {
bool empty() const { return models_.empty(); }
- FeatureFunction::Features all_features(std::ostream *warnings=0); // this will warn about duplicate features as well (one function overwrites the feature of another). also resizes weights_ so it is large enough to hold the (0) weight for the largest reported feature id
+ bool stateless() const { return !state_size_; }
+ FeatureFunction::Features all_features(std::ostream *warnings=0,bool warn_fid_zero=false); // this will warn about duplicate features as well (one function overwrites the feature of another). also resizes weights_ so it is large enough to hold the (0) weight for the largest reported feature id. since 0 is a NULL feature id, it's never included. if warn_fid_zero, then even the first 0 id is
void show_features(std::ostream &out,std::ostream &warn,bool warn_zero_wt=true); //show features and weights
private:
std::vector<const FeatureFunction*> models_;
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-21 15:37:51 +0000