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-rw-r--r--decoder/cdec.cc126
-rw-r--r--decoder/cdec_ff.cc9
-rw-r--r--decoder/ff.cc64
-rw-r--r--decoder/ff.h30
-rw-r--r--decoder/ff_factory.cc1
-rw-r--r--decoder/ff_lm.cc4
-rw-r--r--decoder/ff_lm.h1
7 files changed, 181 insertions, 54 deletions
diff --git a/decoder/cdec.cc b/decoder/cdec.cc
index 54e24792..919751a2 100644
--- a/decoder/cdec.cc
+++ b/decoder/cdec.cc
@@ -56,7 +56,28 @@ void ConvertSV(const SparseVector<prob_t>& src, SparseVector<double>* trg) {
trg->set_value(it->first, it->second);
}
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+
+inline string str(char const* name,po::variables_map const& conf) {
+ return conf[name].as<string>();
+}
+
+shared_ptr<FeatureFunction> make_ff(string const& ffp,bool verbose_feature_functions,char const* pre="") {
+ string ff, param;
+ SplitCommandAndParam(ffp, &ff, &param);
+ cerr << "Feature: " << ff;
+ if (param.size() > 0) cerr << " (with config parameters '" << param << "')\n";
+ else cerr << " (no config parameters)\n";
+ shared_ptr<FeatureFunction> pf = global_ff_registry->Create(ff, param);
+ if (!pf)
+ exit(1);
+ int nbyte=pf->NumBytesContext();
+ if (verbose_feature_functions)
+ cerr<<"State is "<<nbyte<<" bytes for "<<pre<<"feature "<<ffp<<endl;
+ return pf;
+}
+
+void InitCommandLine(int argc, char** argv, po::variables_map* confp) {
+ po::variables_map &conf=*confp;
po::options_description opts("Configuration options");
opts.add_options()
("formalism,f",po::value<string>(),"Decoding formalism; values include SCFG, FST, PB, LexTrans (lexical translation model, also disc training), CSplit (compound splitting), Tagger (sequence labeling), LexAlign (alignment only, or EM training)")
@@ -65,8 +86,9 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
("weights,w",po::value<string>(),"Feature weights file")
("prelm_weights",po::value<string>(),"Feature weights file for prelm_beam_prune. Requires --weights.")
("prelm_copy_weights","use --weights as value for --prelm_weights.")
+ ("prelm_feature_function",po::value<vector<string> >()->composing(),"Additional feature functions for prelm pass only (in addition to the 0-state subset of feature_function")
("keep_prelm_cube_order","when forest rescoring with final models, use the edge ordering from the prelm pruning features*weights. only meaningful if --prelm_weights given. UNTESTED but assume that cube pruning gives a sensible result, and that 'good' (as tuned for bleu w/ prelm features) edges come first.")
-
+ ("warn_0_weight","Warn about any feature id that has a 0 weight (this is perfectly safe if you intend 0 weight, though)")
("no_freeze_feature_set,Z", "Do not freeze feature set after reading feature weights file")
("feature_function,F",po::value<vector<string> >()->composing(), "Additional feature function(s) (-L for list)")
("list_feature_functions,L","List available feature functions")
@@ -111,33 +133,44 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description clo("Command line options");
clo.add_options()
("config,c", po::value<string>(), "Configuration file")
- ("help,h", "Print this help message and exit");
+ ("help,h", "Print this help message and exit")
+ ("usage", po::value<string>(), "Describe a feature function type")
+ ;
+
po::options_description dconfig_options, dcmdline_options;
dconfig_options.add(opts);
dcmdline_options.add(opts).add(clo);
- po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
- if (conf->count("config")) {
- const string cfg = (*conf)["config"].as<string>();
+ po::store(parse_command_line(argc, argv, dcmdline_options), conf);
+ if (conf.count("config")) {
+ const string cfg = str("config",conf);
cerr << "Configuration file: " << cfg << endl;
ifstream config(cfg.c_str());
- po::store(po::parse_config_file(config, dconfig_options), *conf);
+ po::store(po::parse_config_file(config, dconfig_options), conf);
}
- po::notify(*conf);
+ po::notify(conf);
- if (conf->count("list_feature_functions")) {
+ if (conf.count("list_feature_functions")) {
cerr << "Available feature functions (specify with -F):\n";
global_ff_registry->DisplayList();
cerr << endl;
exit(1);
}
- if (conf->count("help") || conf->count("formalism") == 0) {
+ if (conf.count("usage")) {
+ cout<<global_ff_registry->usage(str("usage",conf),true,true)<<endl;
+ exit(0);
+ }
+ if (conf.count("help")) {
+ cout << dcmdline_options << endl;
+ exit(0);
+ }
+ if (conf.count("help") || conf.count("formalism") == 0) {
cerr << dcmdline_options << endl;
exit(1);
}
- const string formalism = LowercaseString((*conf)["formalism"].as<string>());
+ const string formalism = LowercaseString(str("formalism",conf));
if (formalism != "scfg" && formalism != "fst" && formalism != "lextrans" && formalism != "pb" && formalism != "csplit" && formalism != "tagger" && formalism != "lexalign") {
cerr << "Error: --formalism takes only 'scfg', 'fst', 'pb', 'csplit', 'lextrans', 'lexalign', or 'tagger'\n";
cerr << dcmdline_options << endl;
@@ -256,18 +289,17 @@ bool beam_param(po::variables_map const& conf,string const& name,double *val,boo
bool prelm_weights_string(po::variables_map const& conf,string &s)
{
if (conf.count("prelm_weights")) {
- s=conf["prelm_weights"].as<string>();
+ s=str("prelm_weights",conf);
return true;
}
if (conf.count("prelm_copy_weights")) {
- s=conf["weights"].as<string>();
+ s=str("weights",conf);
return true;
}
return false;
}
-
void forest_stats(Hypergraph &forest,string name,bool show_tree,bool show_features,FeatureWeights *weights=0) {
cerr << viterbi_stats(forest,name,true,show_tree);
if (show_features) {
@@ -305,6 +337,10 @@ void maybe_prune(Hypergraph &forest,po::variables_map const& conf,string nbeam,s
}
}
+void show_models(po::variables_map const& conf,ModelSet &ms,char const* header) {
+ cerr<<header<<": ";
+ ms.show_features(cerr,cerr,conf.count("warn_0_weight"));
+}
int main(int argc, char** argv) {
@@ -322,7 +358,7 @@ int main(int argc, char** argv) {
const bool output_training_vector = (write_gradient || feature_expectations);
boost::shared_ptr<Translator> translator;
- const string formalism = LowercaseString(conf["formalism"].as<string>());
+ const string formalism = LowercaseString(str("formalism",conf));
const bool csplit_preserve_full_word = conf.count("csplit_preserve_full_word");
if (csplit_preserve_full_word &&
(formalism != "csplit" || !(conf.count("beam_prune")||conf.count("density_prune")||conf.count("prelm_beam_prune")||conf.count("prelm_density_prune")))) {
@@ -341,7 +377,7 @@ int main(int argc, char** argv) {
Weights w,prelm_w;
bool has_prelm_models = false;
if (conf.count("weights")) {
- w.InitFromFile(conf["weights"].as<string>());
+ w.InitFromFile(str("weights",conf));
feature_weights.resize(FD::NumFeats());
w.InitVector(&feature_weights);
string plmw;
@@ -350,13 +386,9 @@ int main(int argc, char** argv) {
prelm_w.InitFromFile(plmw);
prelm_feature_weights.resize(FD::NumFeats());
prelm_w.InitVector(&prelm_feature_weights);
- cerr << "prelm_weights: " << FeatureVector(prelm_feature_weights)<<endl;
- }
- 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();
+// cerr << "prelm_weights: " << FeatureVector(prelm_feature_weights)<<endl;
}
+// cerr << "+LM weights: " << FeatureVector(feature_weights)<<endl;
}
// set up translation back end
@@ -378,41 +410,46 @@ int main(int argc, char** argv) {
assert(!"error");
// set up additional scoring features
- vector<shared_ptr<FeatureFunction> > pffs;
+ vector<shared_ptr<FeatureFunction> > pffs,prelm_only_ffs;
vector<const FeatureFunction*> late_ffs,prelm_ffs;
if (conf.count("feature_function") > 0) {
const vector<string>& add_ffs = conf["feature_function"].as<vector<string> >();
for (int i = 0; i < add_ffs.size(); ++i) {
- string ff, param;
- SplitCommandAndParam(add_ffs[i], &ff, &param);
- cerr << "Feature: " << ff;
- if (param.size() > 0) cerr << " (with config parameters '" << param << "')\n";
- else cerr << " (no config parameters)\n";
- shared_ptr<FeatureFunction> pff = global_ff_registry->Create(ff, param);
- FeatureFunction const* p=pff.get();
- if (!p) { exit(1); }
- // TODO check that multiple features aren't trying to set the same fid
- pffs.push_back(pff);
+ pffs.push_back(make_ff(add_ffs[i],verbose_feature_functions));
+ FeatureFunction const* p=pffs.back().get();
late_ffs.push_back(p);
- int nbyte=p->NumBytesContext();
- if (verbose_feature_functions)
- cerr<<"State is "<<nbyte<<" bytes for feature "<<ff<<endl;
if (has_prelm_models) {
- if (nbyte==0)
+ if (p->NumBytesContext()==0)
prelm_ffs.push_back(p);
else
- cerr << "Excluding stateful feature from prelm pruning: "<<ff<<" - state is "<<nbyte<<" bytes."<<endl;
+ cerr << "Excluding stateful feature from prelm pruning: "<<add_ffs[i]<<endl;
}
}
}
+ if (conf.count("prelm_feature_function") > 0) {
+ const vector<string>& add_ffs = conf["prelm_feature_function"].as<vector<string> >();
+ for (int i = 0; i < add_ffs.size(); ++i) {
+ prelm_only_ffs.push_back(make_ff(add_ffs[i],verbose_feature_functions,"prelm-only "));
+ prelm_ffs.push_back(prelm_only_ffs.back().get());
+ }
+ }
+
if (has_prelm_models)
cerr << "prelm rescoring with "<<prelm_ffs.size()<<" 0-state feature functions. +LM pass will use "<<late_ffs.size()<<" features (not counting rule features)."<<endl;
ModelSet late_models(feature_weights, late_ffs);
+ show_models(conf,late_models,"late ");
+ 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(conf["intersection_strategy"].as<string>()) == "full") {
+ if (LowercaseString(str("intersection_strategy",conf)) == "full") {
palg = 0;
cerr << "Using full intersection (no pruning).\n";
}
@@ -426,17 +463,17 @@ int main(int argc, char** argv) {
const bool minimal_forests = conf.count("minimal_forests");
const bool graphviz = conf.count("graphviz");
const bool joshua_viz = conf.count("show_joshua_visualization");
- const bool encode_b64 = conf["vector_format"].as<string>() == "b64";
+ const bool encode_b64 = str("vector_format",conf) == "b64";
const bool kbest = conf.count("k_best");
const bool unique_kbest = conf.count("unique_k_best");
const bool crf_uniform_empirical = conf.count("crf_uniform_empirical");
shared_ptr<WriteFile> extract_file;
if (conf.count("extract_rules"))
- extract_file.reset(new WriteFile(conf["extract_rules"].as<string>()));
+ extract_file.reset(new WriteFile(str("extract_rules",conf)));
int combine_size = conf["combine_size"].as<int>();
if (combine_size < 1) combine_size = 1;
- const string input = conf["input"].as<string>();
+ const string input = str("input",conf);
cerr << "Reading input from " << ((input == "-") ? "STDIN" : input.c_str()) << endl;
ReadFile in_read(input);
istream *in = in_read.stream();
@@ -506,7 +543,6 @@ int main(int argc, char** argv) {
ExtractRulesDedupe(forest, extract_file->stream());
if (has_prelm_models) {
- ModelSet prelm_models(prelm_feature_weights, prelm_ffs);
Timer t("prelm rescoring");
forest.Reweight(prelm_feature_weights);
forest.SortInEdgesByEdgeWeights();
@@ -544,7 +580,7 @@ int main(int argc, char** argv) {
maybe_prune(forest,conf,"beam_prune","density_prune","+LM",srclen);
if (conf.count("forest_output") && !has_ref) {
- ForestWriter writer(conf["forest_output"].as<string>(), sent_id);
+ ForestWriter writer(str("forest_output",conf), sent_id);
if (FileExists(writer.fname_)) {
cerr << " Unioning...\n";
Hypergraph new_hg;
@@ -621,7 +657,7 @@ int main(int argc, char** argv) {
}
//DumpKBest(sent_id, forest, 1000);
if (conf.count("forest_output")) {
- ForestWriter writer(conf["forest_output"].as<string>(), sent_id);
+ ForestWriter writer(str("forest_output",conf), sent_id);
if (FileExists(writer.fname_)) {
cerr << " Unioning...\n";
Hypergraph new_hg;
diff --git a/decoder/cdec_ff.cc b/decoder/cdec_ff.cc
index 8cf2f2fd..077956a8 100644
--- a/decoder/cdec_ff.cc
+++ b/decoder/cdec_ff.cc
@@ -12,13 +12,14 @@ boost::shared_ptr<FFRegistry> global_ff_registry;
void register_feature_functions() {
global_ff_registry->Register(new FFFactory<LanguageModel>);
- //TODO: define usage(false,false) for each of the below
+
+ //TODO: use for all features the new Register which requires usage(...)
#ifdef HAVE_RANDLM
global_ff_registry->Register("RandLM", new FFFactory<LanguageModelRandLM>);
#endif
- global_ff_registry->Register("WordPenalty", new FFFactory<WordPenalty>);
- global_ff_registry->Register("SourceWordPenalty", new FFFactory<SourceWordPenalty>);
- global_ff_registry->Register("ArityPenalty", new FFFactory<ArityPenalty>);
+ global_ff_registry->Register(new FFFactory<WordPenalty>);
+ global_ff_registry->Register(new FFFactory<SourceWordPenalty>);
+ global_ff_registry->Register(new FFFactory<ArityPenalty>);
global_ff_registry->Register("RuleShape", new FFFactory<RuleShapeFeatures>);
global_ff_registry->Register("RelativeSentencePosition", new FFFactory<RelativeSentencePosition>);
global_ff_registry->Register("Model2BinaryFeatures", new FFFactory<Model2BinaryFeatures>);
diff --git a/decoder/ff.cc b/decoder/ff.cc
index 73dbbdc9..3f433dfb 100644
--- a/decoder/ff.cc
+++ b/decoder/ff.cc
@@ -29,6 +29,55 @@ string FeatureFunction::usage_helper(std::string const& name,std::string const&
return r;
}
+FeatureFunction::Features FeatureFunction::single_feature(WordID feat) {
+ return Features(1,feat);
+}
+
+FeatureFunction::Features ModelSet::all_features(std::ostream *warn) {
+ typedef FeatureFunction::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?");
+ }
+ for (unsigned j=0;j<si.size();++j) {
+ WordID fid=si[j];
+ 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);
+ }
+ }
+ }
+ return ffs;
+#undef WARNFF
+}
+
+void ModelSet::show_features(std::ostream &out,std::ostream &warn,bool warn_zero_wt)
+{
+ typedef FeatureFunction::Features FFS;
+ FFS ffs=all_features(&warn);
+ 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")),
@@ -59,6 +108,15 @@ SourceWordPenalty::SourceWordPenalty(const string& param) :
}
}
+FeatureFunction::Features SourceWordPenalty::features() const {
+ return single_feature(fid_);
+}
+
+FeatureFunction::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,
@@ -75,12 +133,16 @@ void SourceWordPenalty::TraversalFeaturesImpl(const SentenceMetadata& smeta,
ArityPenalty::ArityPenalty(const std::string& /* param */) :
value_(-1.0 / log(10)) {
string fname = "Arity_X";
- for (int i = 0; i < 10; ++i) {
+ for (int i = 0; i < N_ARITIES; ++i) {
fname[6]=i + '0';
fids_[i] = FD::Convert(fname);
}
}
+FeatureFunction::Features ArityPenalty::features() const {
+ return Features(&fids_[0],&fids_[N_ARITIES]);
+}
+
void ArityPenalty::TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const std::vector<const void*>& ant_states,
diff --git a/decoder/ff.h b/decoder/ff.h
index c6c9cf8f..6f8b8626 100644
--- a/decoder/ff.h
+++ b/decoder/ff.h
@@ -15,6 +15,7 @@ class FeatureFunction; // see definition below
// FinalTraversalFeatures(...)
class FeatureFunction {
public:
+ std::string name; // set by FF factory using usage()
FeatureFunction() : state_size_() {}
explicit FeatureFunction(int state_size) : state_size_(state_size) {}
virtual ~FeatureFunction();
@@ -24,12 +25,14 @@ class FeatureFunction {
return usage_helper("FIXME_feature_needs_name","[no parameters]","[no documentation yet]",show_params,show_details);
}
- static std::string usage_helper(std::string const& name,std::string const& params,std::string const& details,bool show_params,bool show_details);
+ typedef std::vector<WordID> Features; // set of features ids
+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:
- typedef std::vector<WordID> Features;
- virtual Features features() { return Features(); }
+ 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).
// NOTE: this value is fixed for the instance of your class, you cannot
@@ -87,7 +90,11 @@ public:
// add value_
class WordPenalty : public FeatureFunction {
public:
+ Features features() const;
WordPenalty(const std::string& param);
+ static std::string usage(bool p,bool d) {
+ return usage_helper("WordPenalty","","number of target words (local feature)",p,d);
+ }
protected:
virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
@@ -102,7 +109,11 @@ class WordPenalty : public FeatureFunction {
class SourceWordPenalty : public FeatureFunction {
public:
+ Features features() const;
SourceWordPenalty(const std::string& param);
+ static std::string usage(bool p,bool d) {
+ return usage_helper("SourceWordPenalty","","number of source words (local feature, and meaningless except when input has non-constant number of source words, e.g. segmentation/morphology/speech recognition lattice)",p,d);
+ }
protected:
virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
@@ -117,7 +128,12 @@ class SourceWordPenalty : public FeatureFunction {
class ArityPenalty : public FeatureFunction {
public:
+ 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);
+ }
+
protected:
virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
@@ -126,7 +142,10 @@ class ArityPenalty : public FeatureFunction {
SparseVector<double>* estimated_features,
void* context) const;
private:
- int fids_[10];
+ enum {N_ARITIES=10};
+
+
+ int fids_[N_ARITIES];
const double value_;
};
@@ -153,6 +172,9 @@ class ModelSet {
Hypergraph::Edge* edge) const;
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
+ void show_features(std::ostream &out,std::ostream &warn,bool warn_zero_wt=true); //show features and weights
private:
std::vector<const FeatureFunction*> models_;
std::vector<double> weights_;
diff --git a/decoder/ff_factory.cc b/decoder/ff_factory.cc
index d66cd883..fe733ca5 100644
--- a/decoder/ff_factory.cc
+++ b/decoder/ff_factory.cc
@@ -28,6 +28,7 @@ shared_ptr<FeatureFunction> FFRegistry::Create(const string& ffname, const strin
cerr << "I don't know how to create feature " << ffname << endl;
} else {
res = it->second->Create(param);
+ res->name=ffname;
}
return res;
}
diff --git a/decoder/ff_lm.cc b/decoder/ff_lm.cc
index 9e6f02b7..0590fa7e 100644
--- a/decoder/ff_lm.cc
+++ b/decoder/ff_lm.cc
@@ -532,6 +532,10 @@ LanguageModel::LanguageModel(const string& param) {
SetStateSize(LanguageModelImpl::OrderToStateSize(order));
}
+FeatureFunction::Features LanguageModel::features() const {
+ return single_feature(fid_);
+}
+
LanguageModel::~LanguageModel() {
delete pimpl_;
}
diff --git a/decoder/ff_lm.h b/decoder/ff_lm.h
index 5ea41068..935e283c 100644
--- a/decoder/ff_lm.h
+++ b/decoder/ff_lm.h
@@ -19,6 +19,7 @@ class LanguageModel : public FeatureFunction {
SparseVector<double>* features) const;
std::string DebugStateToString(const void* state) const;
static std::string usage(bool param,bool verbose);
+ Features features() const;
protected:
virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,