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-rw-r--r--gi/pf/Makefile.am2
-rw-r--r--gi/pf/corpus.cc57
-rw-r--r--gi/pf/corpus.h19
-rw-r--r--gi/pf/dpnaive.cc95
-rw-r--r--gi/pf/monotonic_pseg.h88
-rw-r--r--gi/pf/pfnaive.cc116
6 files changed, 202 insertions, 175 deletions
diff --git a/gi/pf/Makefile.am b/gi/pf/Makefile.am
index c9764ad5..42758939 100644
--- a/gi/pf/Makefile.am
+++ b/gi/pf/Makefile.am
@@ -1,7 +1,7 @@
bin_PROGRAMS = cbgi brat dpnaive pfbrat pfdist itg pfnaive
noinst_LIBRARIES = libpf.a
-libpf_a_SOURCES = base_measures.cc reachability.cc cfg_wfst_composer.cc
+libpf_a_SOURCES = base_measures.cc reachability.cc cfg_wfst_composer.cc corpus.cc
itg_SOURCES = itg.cc
diff --git a/gi/pf/corpus.cc b/gi/pf/corpus.cc
new file mode 100644
index 00000000..a408e7cf
--- /dev/null
+++ b/gi/pf/corpus.cc
@@ -0,0 +1,57 @@
+#include "corpus.h"
+
+#include <set>
+#include <vector>
+#include <string>
+
+#include "tdict.h"
+#include "filelib.h"
+
+using namespace std;
+
+namespace corpus {
+
+void ReadParallelCorpus(const string& filename,
+ vector<vector<WordID> >* f,
+ vector<vector<WordID> >* e,
+ set<WordID>* vocab_f,
+ set<WordID>* vocab_e) {
+ f->clear();
+ e->clear();
+ vocab_f->clear();
+ vocab_e->clear();
+ ReadFile rf(filename);
+ istream* in = rf.stream();
+ assert(*in);
+ string line;
+ const WordID kDIV = TD::Convert("|||");
+ vector<WordID> tmp;
+ while(*in) {
+ getline(*in, line);
+ if (line.empty() && !*in) break;
+ e->push_back(vector<int>());
+ f->push_back(vector<int>());
+ vector<int>& le = e->back();
+ vector<int>& lf = f->back();
+ tmp.clear();
+ TD::ConvertSentence(line, &tmp);
+ bool isf = true;
+ for (unsigned i = 0; i < tmp.size(); ++i) {
+ const int cur = tmp[i];
+ if (isf) {
+ if (kDIV == cur) { isf = false; } else {
+ lf.push_back(cur);
+ vocab_f->insert(cur);
+ }
+ } else {
+ assert(cur != kDIV);
+ le.push_back(cur);
+ vocab_e->insert(cur);
+ }
+ }
+ assert(isf == false);
+ }
+}
+
+}
+
diff --git a/gi/pf/corpus.h b/gi/pf/corpus.h
new file mode 100644
index 00000000..e7febdb7
--- /dev/null
+++ b/gi/pf/corpus.h
@@ -0,0 +1,19 @@
+#ifndef _CORPUS_H_
+#define _CORPUS_H_
+
+#include <string>
+#include <vector>
+#include <set>
+#include "wordid.h"
+
+namespace corpus {
+
+void ReadParallelCorpus(const std::string& filename,
+ std::vector<std::vector<WordID> >* f,
+ std::vector<std::vector<WordID> >* e,
+ std::set<WordID>* vocab_f,
+ std::set<WordID>* vocab_e);
+
+}
+
+#endif
diff --git a/gi/pf/dpnaive.cc b/gi/pf/dpnaive.cc
index 608f73d5..c926487b 100644
--- a/gi/pf/dpnaive.cc
+++ b/gi/pf/dpnaive.cc
@@ -7,12 +7,14 @@
#include <boost/program_options/variables_map.hpp>
#include "base_measures.h"
+#include "monotonic_pseg.h"
#include "trule.h"
#include "tdict.h"
#include "filelib.h"
#include "dict.h"
#include "sampler.h"
#include "ccrp_nt.h"
+#include "corpus.h"
using namespace std;
using namespace std::tr1;
@@ -52,57 +54,12 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
}
-void ReadParallelCorpus(const string& filename,
- vector<vector<WordID> >* f,
- vector<vector<int> >* e,
- set<int>* vocab_e,
- set<int>* vocab_f) {
- f->clear();
- e->clear();
- vocab_f->clear();
- vocab_e->clear();
- istream* in;
- if (filename == "-")
- in = &cin;
- else
- in = new ifstream(filename.c_str());
- assert(*in);
- string line;
- const WordID kDIV = TD::Convert("|||");
- vector<WordID> tmp;
- while(*in) {
- getline(*in, line);
- if (line.empty() && !*in) break;
- e->push_back(vector<int>());
- f->push_back(vector<int>());
- vector<int>& le = e->back();
- vector<int>& lf = f->back();
- tmp.clear();
- TD::ConvertSentence(line, &tmp);
- bool isf = true;
- for (unsigned i = 0; i < tmp.size(); ++i) {
- const int cur = tmp[i];
- if (isf) {
- if (kDIV == cur) { isf = false; } else {
- lf.push_back(cur);
- vocab_f->insert(cur);
- }
- } else {
- assert(cur != kDIV);
- le.push_back(cur);
- vocab_e->insert(cur);
- }
- }
- assert(isf == false);
- }
- if (in != &cin) delete in;
-}
-
shared_ptr<MT19937> prng;
template <typename Base>
struct ModelAndData {
- explicit ModelAndData(const Base& b, const vector<vector<int> >& ce, const vector<vector<int> >& cf, const set<int>& ve, const set<int>& vf) :
+ explicit ModelAndData(MonotonicParallelSegementationModel& m, const Base& b, const vector<vector<int> >& ce, const vector<vector<int> >& cf, const set<int>& ve, const set<int>& vf) :
+ model(m),
rng(&*prng),
p0(b),
baseprob(prob_t::One()),
@@ -110,14 +67,12 @@ struct ModelAndData {
corpusf(cf),
vocabe(ve),
vocabf(vf),
- rules(1,1),
mh_samples(),
mh_rejects(),
kX(-TD::Convert("X")),
derivations(corpuse.size()) {}
void ResampleHyperparameters() {
- rules.resample_hyperparameters(&*prng);
}
void InstantiateRule(const pair<short,short>& from,
@@ -139,12 +94,10 @@ struct ModelAndData {
TRule x;
for (int i = 1; i < d.size(); ++i) {
InstantiateRule(d[i], d[i-1], sentf, sente, &x);
- //cerr << "REMOVE: " << x.AsString() << endl;
- if (rules.decrement(x)) {
- baseprob /= p0(x);
- //cerr << " (REMOVED ONLY INSTANCE)\n";
- }
+ model.DecrementRule(x);
+ model.DecrementContinue();
}
+ model.DecrementStop();
}
void PrintDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) {
@@ -161,39 +114,38 @@ struct ModelAndData {
TRule x;
for (int i = 1; i < d.size(); ++i) {
InstantiateRule(d[i], d[i-1], sentf, sente, &x);
- if (rules.increment(x)) {
- baseprob *= p0(x);
- }
+ model.IncrementRule(x);
+ model.IncrementContinue();
}
+ model.IncrementStop();
}
prob_t Likelihood() const {
- prob_t p;
- p.logeq(rules.log_crp_prob());
- return p * baseprob;
+ return model.Likelihood();
}
prob_t DerivationProposalProbability(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) const {
- prob_t p = prob_t::One();
+ prob_t p = model.StopProbability();
if (d.size() < 2) return p;
TRule x;
+ const prob_t p_cont = model.ContinueProbability();
for (int i = 1; i < d.size(); ++i) {
InstantiateRule(d[i], d[i-1], sentf, sente, &x);
- prob_t rp; rp.logeq(rules.logprob(x, log(p0(x))));
- p *= rp;
+ p *= p_cont;
+ p *= model.RuleProbability(x);
}
return p;
}
void Sample();
+ MonotonicParallelSegementationModel& model;
MT19937* rng;
const Base& p0;
prob_t baseprob; // cached value of generating the table table labels from p0
// this can't be used if we go to a hierarchical prior!
const vector<vector<int> >& corpuse, corpusf;
const set<int>& vocabe, vocabf;
- CCRP_NoTable<TRule> rules;
unsigned mh_samples, mh_rejects;
const int kX;
vector<vector<pair<short, short> > > derivations;
@@ -201,8 +153,8 @@ struct ModelAndData {
template <typename Base>
void ModelAndData<Base>::Sample() {
- unsigned MAXK = 4;
- unsigned MAXL = 4;
+ unsigned MAXK = kMAX_SRC_PHRASE;
+ unsigned MAXL = kMAX_TRG_PHRASE;
TRule x;
x.lhs_ = -TD::Convert("X");
for (int samples = 0; samples < 1000; ++samples) {
@@ -228,6 +180,8 @@ void ModelAndData<Base>::Sample() {
boost::multi_array<prob_t, 2> a(boost::extents[sentf.size() + 1][sente.size() + 1]);
boost::multi_array<prob_t, 4> trans(boost::extents[sentf.size() + 1][sente.size() + 1][MAXK][MAXL]);
a[0][0] = prob_t::One();
+ const prob_t q_stop = model.StopProbability();
+ const prob_t q_cont = model.ContinueProbability();
for (int i = 0; i < sentf.size(); ++i) {
for (int j = 0; j < sente.size(); ++j) {
const prob_t src_a = a[i][j];
@@ -239,7 +193,9 @@ void ModelAndData<Base>::Sample() {
for (int l = 1; l <= MAXL; ++l) {
if (j + l > sente.size()) break;
x.e_.push_back(sente[j + l - 1]);
- trans[i][j][k - 1][l - 1].logeq(rules.logprob(x, log(p0(x))));
+ const bool stop_now = ((j + l) == sente.size()) && ((i + k) == sentf.size());
+ const prob_t& cp = stop_now ? q_stop : q_cont;
+ trans[i][j][k - 1][l - 1] = model.RuleProbability(x) * cp;
a[i + k][j + l] += src_a * trans[i][j][k - 1][l - 1];
}
}
@@ -319,7 +275,7 @@ int main(int argc, char** argv) {
vector<vector<int> > corpuse, corpusf;
set<int> vocabe, vocabf;
- ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
+ corpus::ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
cerr << "f-Corpus size: " << corpusf.size() << " sentences\n";
cerr << "f-Vocabulary size: " << vocabf.size() << " types\n";
cerr << "f-Corpus size: " << corpuse.size() << " sentences\n";
@@ -328,8 +284,9 @@ int main(int argc, char** argv) {
Model1 m1(conf["model1"].as<string>());
PhraseJointBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
+ MonotonicParallelSegementationModel m(lp0);
- ModelAndData<PhraseJointBase> posterior(lp0, corpuse, corpusf, vocabe, vocabf);
+ ModelAndData<PhraseJointBase> posterior(m, lp0, corpuse, corpusf, vocabe, vocabf);
posterior.Sample();
return 0;
diff --git a/gi/pf/monotonic_pseg.h b/gi/pf/monotonic_pseg.h
new file mode 100644
index 00000000..7e6af3fc
--- /dev/null
+++ b/gi/pf/monotonic_pseg.h
@@ -0,0 +1,88 @@
+#ifndef _MONOTONIC_PSEG_H_
+#define _MONOTONIC_PSEG_H_
+
+#include <vector>
+
+#include "prob.h"
+#include "ccrp_nt.h"
+#include "trule.h"
+#include "base_measures.h"
+
+struct MonotonicParallelSegementationModel {
+ explicit MonotonicParallelSegementationModel(PhraseJointBase& rcp0) :
+ rp0(rcp0), base(prob_t::One()), rules(1,1), stop(1.0) {}
+
+ void DecrementRule(const TRule& rule) {
+ if (rules.decrement(rule))
+ base /= rp0(rule);
+ }
+
+ void IncrementRule(const TRule& rule) {
+ if (rules.increment(rule))
+ base *= rp0(rule);
+ }
+
+ void IncrementRulesAndStops(const std::vector<TRulePtr>& rules) {
+ for (int i = 0; i < rules.size(); ++i)
+ IncrementRule(*rules[i]);
+ if (rules.size()) IncrementContinue(rules.size() - 1);
+ IncrementStop();
+ }
+
+ void DecrementRulesAndStops(const std::vector<TRulePtr>& rules) {
+ for (int i = 0; i < rules.size(); ++i)
+ DecrementRule(*rules[i]);
+ if (rules.size()) {
+ DecrementContinue(rules.size() - 1);
+ DecrementStop();
+ }
+ }
+
+ prob_t RuleProbability(const TRule& rule) const {
+ prob_t p; p.logeq(rules.logprob(rule, log(rp0(rule))));
+ return p;
+ }
+
+ prob_t Likelihood() const {
+ prob_t p = base;
+ prob_t q; q.logeq(rules.log_crp_prob());
+ p *= q;
+ q.logeq(stop.log_crp_prob());
+ p *= q;
+ return p;
+ }
+
+ void IncrementStop() {
+ stop.increment(true);
+ }
+
+ void IncrementContinue(int n = 1) {
+ for (int i = 0; i < n; ++i)
+ stop.increment(false);
+ }
+
+ void DecrementStop() {
+ stop.decrement(true);
+ }
+
+ void DecrementContinue(int n = 1) {
+ for (int i = 0; i < n; ++i)
+ stop.decrement(false);
+ }
+
+ prob_t StopProbability() const {
+ return prob_t(stop.prob(true, 0.5));
+ }
+
+ prob_t ContinueProbability() const {
+ return prob_t(stop.prob(false, 0.5));
+ }
+
+ const PhraseJointBase& rp0;
+ prob_t base;
+ CCRP_NoTable<TRule> rules;
+ CCRP_NoTable<bool> stop;
+};
+
+#endif
+
diff --git a/gi/pf/pfnaive.cc b/gi/pf/pfnaive.cc
index c30e7c4f..33dc08c3 100644
--- a/gi/pf/pfnaive.cc
+++ b/gi/pf/pfnaive.cc
@@ -7,6 +7,7 @@
#include <boost/program_options/variables_map.hpp>
#include "base_measures.h"
+#include "monotonic_pseg.h"
#include "reachability.h"
#include "viterbi.h"
#include "hg.h"
@@ -17,6 +18,7 @@
#include "sampler.h"
#include "ccrp_nt.h"
#include "ccrp_onetable.h"
+#include "corpus.h"
using namespace std;
using namespace tr1;
@@ -58,101 +60,6 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
}
-void ReadParallelCorpus(const string& filename,
- vector<vector<WordID> >* f,
- vector<vector<WordID> >* e,
- set<WordID>* vocab_f,
- set<WordID>* vocab_e) {
- f->clear();
- e->clear();
- vocab_f->clear();
- vocab_e->clear();
- istream* in;
- if (filename == "-")
- in = &cin;
- else
- in = new ifstream(filename.c_str());
- assert(*in);
- string line;
- const WordID kDIV = TD::Convert("|||");
- vector<WordID> tmp;
- while(*in) {
- getline(*in, line);
- if (line.empty() && !*in) break;
- e->push_back(vector<int>());
- f->push_back(vector<int>());
- vector<int>& le = e->back();
- vector<int>& lf = f->back();
- tmp.clear();
- TD::ConvertSentence(line, &tmp);
- bool isf = true;
- for (unsigned i = 0; i < tmp.size(); ++i) {
- const int cur = tmp[i];
- if (isf) {
- if (kDIV == cur) { isf = false; } else {
- lf.push_back(cur);
- vocab_f->insert(cur);
- }
- } else {
- assert(cur != kDIV);
- le.push_back(cur);
- vocab_e->insert(cur);
- }
- }
- assert(isf == false);
- }
- if (in != &cin) delete in;
-}
-
-struct MyJointModel {
- MyJointModel(PhraseJointBase& rcp0) :
- rp0(rcp0), base(prob_t::One()), rules(1,1) {}
-
- void DecrementRule(const TRule& rule) {
- if (rules.decrement(rule))
- base /= rp0(rule);
- }
-
- void IncrementRule(const TRule& rule) {
- if (rules.increment(rule))
- base *= rp0(rule);
- }
-
- void IncrementRules(const vector<TRulePtr>& rules) {
- for (int i = 0; i < rules.size(); ++i)
- IncrementRule(*rules[i]);
- }
-
- void DecrementRules(const vector<TRulePtr>& rules) {
- for (int i = 0; i < rules.size(); ++i)
- DecrementRule(*rules[i]);
- }
-
- prob_t RuleProbability(const TRule& rule) const {
- prob_t p; p.logeq(rules.logprob(rule, log(rp0(rule))));
- return p;
- }
-
- prob_t Likelihood() const {
- prob_t p = base;
- prob_t q; q.logeq(rules.log_crp_prob());
- p *= q;
- for (unsigned l = 1; l < src_jumps.size(); ++l) {
- if (src_jumps[l].num_customers() > 0) {
- prob_t q;
- q.logeq(src_jumps[l].log_crp_prob());
- p *= q;
- }
- }
- return p;
- }
-
- const PhraseJointBase& rp0;
- prob_t base;
- CCRP_NoTable<TRule> rules;
- vector<CCRP_NoTable<int> > src_jumps;
-};
-
struct BackwardEstimateSym {
BackwardEstimateSym(const Model1& m1,
const Model1& invm1, const vector<WordID>& src, const vector<WordID>& trg) :
@@ -264,7 +171,7 @@ int main(int argc, char** argv) {
vector<vector<WordID> > corpuse, corpusf;
set<WordID> vocabe, vocabf;
cerr << "Reading corpus...\n";
- ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
+ corpus::ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
cerr << "F-corpus size: " << corpusf.size() << " sentences\t (" << vocabf.size() << " word types)\n";
cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
assert(corpusf.size() == corpuse.size());
@@ -273,13 +180,8 @@ int main(int argc, char** argv) {
Model1 m1(conf["model1"].as<string>());
Model1 invm1(conf["inverse_model1"].as<string>());
-#if 0
- PhraseConditionalBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size());
- MyConditionalModel m(lp0);
-#else
PhraseJointBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
- MyJointModel m(lp0);
-#endif
+ MonotonicParallelSegementationModel m(lp0);
cerr << "Initializing reachability limits...\n";
vector<Particle> ps(corpusf.size());
@@ -296,7 +198,10 @@ int main(int argc, char** argv) {
for (int ci = 0; ci < corpusf.size(); ++ci) {
vector<int>& src = corpusf[ci];
vector<int>& trg = corpuse[ci];
- m.DecrementRules(ps[ci].rules);
+ m.DecrementRulesAndStops(ps[ci].rules);
+ const prob_t q_stop = m.StopProbability();
+ const prob_t q_cont = m.ContinueProbability();
+ cerr << "P(stop)=" << q_stop << "\tP(continue)=" <<q_cont << endl;
BackwardEstimateSym be(m1, invm1, src, trg);
const Reachability& r = reaches[ci];
@@ -336,7 +241,8 @@ int main(int argc, char** argv) {
x.f_.push_back(src[i + j]);
np.src_cov += x.f_.size();
np.trg_cov += x.e_.size();
- prob_t rp = m.RuleProbability(x);
+ const bool stop_now = (np.src_cov == src_len && np.trg_cov == trg_len);
+ prob_t rp = m.RuleProbability(x) * (stop_now ? q_stop : q_cont);
np.gamma_last = rp;
const prob_t u = pow(np.gamma_last * pow(be(np.src_cov, np.trg_cov), 1.2), 0.1);
//cerr << "**rule=" << x << endl;
@@ -363,7 +269,7 @@ int main(int argc, char** argv) {
pfss.add(lps[i].weight);
const int sampled = rng.SelectSample(pfss);
ps[ci] = lps[sampled];
- m.IncrementRules(lps[sampled].rules);
+ m.IncrementRulesAndStops(lps[sampled].rules);
for (int i = 0; i < lps[sampled].rules.size(); ++i) { cerr << "S:\t" << lps[sampled].rules[i]->AsString() << "\n"; }
cerr << "tmp-LLH: " << log(m.Likelihood()) << endl;
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-11 06:11:01 +0000