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-rw-r--r--extools/featurize_grammar.cc156
1 files changed, 126 insertions, 30 deletions
diff --git a/extools/featurize_grammar.cc b/extools/featurize_grammar.cc
index b387fe04..27c0dadf 100644
--- a/extools/featurize_grammar.cc
+++ b/extools/featurize_grammar.cc
@@ -5,6 +5,7 @@
#include <sstream>
#include <string>
#include <map>
+#include <set>
#include <vector>
#include <utility>
#include <cstdlib>
@@ -376,7 +377,7 @@ struct LogRuleCount : public FeatureExtractor {
SparseVector<float>* result) const {
(void) lhs; (void) src; (void) trg;
//result->set_value(fid_, log(info.counts.value(kCFE)));
- result->set_value(fid_, (info.counts.value(kCFE)));
+ result->set_value(fid_, log(info.counts.value(kCFE)));
if (IsZero(info.counts.value(kCFE)))
result->set_value(sfid_, 1);
}
@@ -385,13 +386,25 @@ struct LogRuleCount : public FeatureExtractor {
const int kCFE;
};
+struct RulePenalty : public FeatureExtractor {
+ RulePenalty() : fid_(FD::Convert("RulePenalty")) {}
+ virtual void ExtractFeatures(const WordID /*lhs*/,
+ const vector<WordID>& /*src*/,
+ const vector<WordID>& /*trg*/,
+ const RuleStatistics& /*info*/,
+ SparseVector<float>* result) const
+ { result->set_value(fid_, 1); }
+
+ const int fid_;
+};
+
struct BackoffRule : public FeatureExtractor {
- BackoffRule() :
- fid_(FD::Convert("BackoffRule")) {}
+ BackoffRule() : fid_(FD::Convert("BackoffRule")) {}
+
virtual void ExtractFeatures(const WordID lhs,
const vector<WordID>& src,
const vector<WordID>& trg,
- const RuleStatistics& info,
+ const RuleStatistics& /*info*/,
SparseVector<float>* result) const {
(void) lhs; (void) src; (void) trg;
string lhstr = TD::Convert(lhs);
@@ -404,6 +417,7 @@ struct BackoffRule : public FeatureExtractor {
// The negative log of the condition rule probs
// ignoring the identities of the non-terminals.
// i.e. the prob Hiero would assign.
+// Also extracts Labelled features.
struct XFeatures: public FeatureExtractor {
XFeatures() :
fid_xfe(FD::Convert("XFE")),
@@ -425,14 +439,11 @@ struct XFeatures: public FeatureExtractor {
target_counts.inc(r.target(), 0);
}
- // compute statistics over keys, the same lhs-src-trg tuple may be seen
- // more than once
virtual void ObserveUnfilteredRule(const WordID /*lhs*/,
const vector<WordID>& src,
const vector<WordID>& trg,
const RuleStatistics& info) {
RuleTuple r(-1, src, trg);
-// cerr << " ObserveUnfilteredRule() in:" << r << " " << hash_value(r) << endl;
map_rule(r);
rule_counts.inc_if_exists(r, info.counts.value(kCFE));
@@ -441,7 +452,6 @@ struct XFeatures: public FeatureExtractor {
normalise_string(r.target());
target_counts.inc_if_exists(r.target(), info.counts.value(kCFE));
-// cerr << " ObserveUnfilteredRule() inc: " << r << " " << hash_value(r) << " " << info.counts.value(kCFE) << " to " << rule_counts(r) << endl;
}
virtual void ExtractFeatures(const WordID /*lhs*/,
@@ -451,18 +461,15 @@ struct XFeatures: public FeatureExtractor {
SparseVector<float>* result) const {
RuleTuple r(-1, src, trg);
map_rule(r);
- //result->set_value(fid_fe, rule_counts(r));
double l_r_freq = log(rule_counts(r));
normalise_string(r.target());
result->set_value(fid_xfe, log(target_counts(r.target())) - l_r_freq);
result->set_value(fid_labelledfe, log(target_counts(r.target())) - log(info.counts.value(kCFE)));
- //result->set_value(fid_labelledfe, target_counts(r.target()));
normalise_string(r.source());
result->set_value(fid_xef, log(source_counts(r.source())) - l_r_freq);
result->set_value(fid_labelledef, log(source_counts(r.source())) - log(info.counts.value(kCFE)));
- //result->set_value(fid_labelledef, source_counts(r.source()));
}
void map_rule(RuleTuple& r) const {
@@ -490,20 +497,17 @@ struct XFeatures: public FeatureExtractor {
FreqCount< vector<WordID> > source_counts, target_counts;
};
+
struct LabelledRuleConditionals: public FeatureExtractor {
LabelledRuleConditionals() :
- fid_count(FD::Convert("TCount")),
- fid_e(FD::Convert("TCountE")),
- fid_f(FD::Convert("TCountF")),
- fid_fe(FD::Convert("TLabelledFE")),
- fid_ef(FD::Convert("TLabelledEF")),
+ fid_fe(FD::Convert("LabelledFE")),
+ fid_ef(FD::Convert("LabelledEF")),
kCFE(FD::Convert("CFE")) {}
virtual void ObserveFilteredRule(const WordID lhs,
const vector<WordID>& src,
const vector<WordID>& trg) {
RuleTuple r(lhs, src, trg);
rule_counts.inc(r, 0);
- //cerr << " ObservefilteredRule() inc: " << r << " " << hash_value(r) << endl;
normalise_string(r.source());
source_counts.inc(r.source(), 0);
@@ -511,15 +515,12 @@ struct LabelledRuleConditionals: public FeatureExtractor {
target_counts.inc(r.target(), 0);
}
- // compute statistics over keys, the same lhs-src-trg tuple may be seen
- // more than once
virtual void ObserveUnfilteredRule(const WordID lhs,
const vector<WordID>& src,
const vector<WordID>& trg,
const RuleStatistics& info) {
RuleTuple r(lhs, src, trg);
rule_counts.inc_if_exists(r, info.counts.value(kCFE));
- //cerr << " ObserveUnfilteredRule() inc_if_exists: " << r << " " << hash_value(r) << " " << info.counts.value(kCFE) << " to " << rule_counts(r) << endl;
normalise_string(r.source());
source_counts.inc_if_exists(r.source(), info.counts.value(kCFE));
@@ -534,19 +535,10 @@ struct LabelledRuleConditionals: public FeatureExtractor {
SparseVector<float>* result) const {
RuleTuple r(lhs, src, trg);
double l_r_freq = log(rule_counts(r));
- //result->set_value(fid_count, rule_counts(r));
- //cerr << " ExtractFeatures() count: " << r << " " << info.counts.value(kCFE) << " | " << rule_counts(r) << endl;
- //assert(l_r_freq == log(info.counts.value(kCFE)));
- //cerr << " ExtractFeatures() after:" << " " << r.hash << endl;
- //cerr << " ExtractFeatures() in:" << r << " " << r_freq << " " << hash_value(r) << endl;
- //cerr << " ExtractFeatures() in:" << r << " " << r_freq << endl;
normalise_string(r.target());
result->set_value(fid_fe, log(target_counts(r.target())) - l_r_freq);
normalise_string(r.source());
result->set_value(fid_ef, log(source_counts(r.source())) - l_r_freq);
-
- //result->set_value(fid_e, target_counts(r.target()));
- //result->set_value(fid_f, source_counts(r.source()));
}
void normalise_string(vector<WordID>& r) const {
@@ -556,12 +548,112 @@ struct LabelledRuleConditionals: public FeatureExtractor {
}
const int fid_fe, fid_ef;
- const int fid_count, fid_e, fid_f;
const int kCFE;
RuleFreqCount rule_counts;
FreqCount< vector<WordID> > source_counts, target_counts;
};
+struct LHSProb: public FeatureExtractor {
+ LHSProb() : fid_(FD::Convert("LHSProb")), kCFE(FD::Convert("CFE")), total_count(0) {}
+
+ virtual void ObserveUnfilteredRule(const WordID lhs,
+ const vector<WordID>& /*src*/,
+ const vector<WordID>& /*trg*/,
+ const RuleStatistics& info) {
+ int count = info.counts.value(kCFE);
+ total_count += count;
+ lhs_counts.inc(lhs, count);
+ }
+
+ virtual void ExtractFeatures(const WordID lhs,
+ const vector<WordID>& /*src*/,
+ const vector<WordID>& /*trg*/,
+ const RuleStatistics& /*info*/,
+ SparseVector<float>* result) const {
+ double lhs_log_prob = log(total_count) - log(lhs_counts(lhs));
+ result->set_value(fid_, lhs_log_prob);
+ }
+
+ const int fid_;
+ const int kCFE;
+ int total_count;
+ FreqCount<WordID> lhs_counts;
+};
+
+// Proper rule generative probability: p( s,t | lhs)
+struct GenerativeProb: public FeatureExtractor {
+ GenerativeProb() :
+ fid_(FD::Convert("GenerativeProb")),
+ kCFE(FD::Convert("CFE")) {}
+
+ virtual void ObserveUnfilteredRule(const WordID lhs,
+ const vector<WordID>& /*src*/,
+ const vector<WordID>& /*trg*/,
+ const RuleStatistics& info)
+ { lhs_counts.inc(lhs, info.counts.value(kCFE)); }
+
+ virtual void ExtractFeatures(const WordID lhs,
+ const vector<WordID>& /*src*/,
+ const vector<WordID>& /*trg*/,
+ const RuleStatistics& info,
+ SparseVector<float>* result) const {
+ double log_prob = log(lhs_counts(lhs)) - log(info.counts.value(kCFE));
+ result->set_value(fid_, log_prob);
+ }
+
+ const int fid_;
+ const int kCFE;
+ FreqCount<WordID> lhs_counts;
+};
+
+// remove terminals from the rules before estimating the conditional prob
+struct LabellingShape: public FeatureExtractor {
+ LabellingShape() : fid_(FD::Convert("LabellingShape")), kCFE(FD::Convert("CFE")) {}
+
+ virtual void ObserveFilteredRule(const WordID /*lhs*/,
+ const vector<WordID>& src,
+ const vector<WordID>& trg) {
+ RuleTuple r(-1, src, trg);
+ map_rule(r);
+ rule_counts.inc(r, 0);
+ source_counts.inc(r.source(), 0);
+ }
+
+ virtual void ObserveUnfilteredRule(const WordID /*lhs*/,
+ const vector<WordID>& src,
+ const vector<WordID>& trg,
+ const RuleStatistics& info) {
+ RuleTuple r(-1, src, trg);
+ map_rule(r);
+ rule_counts.inc_if_exists(r, info.counts.value(kCFE));
+ source_counts.inc_if_exists(r.source(), info.counts.value(kCFE));
+ }
+
+ virtual void ExtractFeatures(const WordID /*lhs*/,
+ const vector<WordID>& src,
+ const vector<WordID>& trg,
+ const RuleStatistics& /*info*/,
+ SparseVector<float>* result) const {
+ RuleTuple r(-1, src, trg);
+ map_rule(r);
+ double l_r_freq = log(rule_counts(r));
+ result->set_value(fid_, log(source_counts(r.source())) - l_r_freq);
+ }
+
+ // Replace all terminals with generic -1
+ void map_rule(RuleTuple& r) const {
+ for (vector<WordID>::iterator it = r.target().begin(); it != r.target().end(); ++it)
+ if (!validate_non_terminal(TD::Convert(*it))) *it = -1;
+ for (vector<WordID>::iterator it = r.source().begin(); it != r.source().end(); ++it)
+ if (!validate_non_terminal(TD::Convert(*it))) *it = -1;
+ }
+
+ const int fid_, kCFE;
+ RuleFreqCount rule_counts;
+ FreqCount< vector<WordID> > source_counts;
+};
+
+
// this extracts the lexical translation prob features
// in BOTH directions.
struct LexProbExtractor : public FeatureExtractor {
@@ -673,6 +765,10 @@ int main(int argc, char** argv){
reg.Register("XFeatures", new FEFactory<XFeatures>);
reg.Register("LabelledRuleConditionals", new FEFactory<LabelledRuleConditionals>);
reg.Register("BackoffRule", new FEFactory<BackoffRule>);
+ reg.Register("RulePenalty", new FEFactory<RulePenalty>);
+ reg.Register("LHSProb", new FEFactory<LHSProb>);
+ reg.Register("LabellingShape", new FEFactory<LabellingShape>);
+ reg.Register("GenerativeProb", new FEFactory<GenerativeProb>);
po::variables_map conf;
InitCommandLine(reg, argc, argv, &conf);
aligned_corpus = conf["aligned_corpus"].as<string>(); // GLOBAL VAR
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-22 12:43:19 +0000