From e26434979adc33bd949566ba7bf02dff64e80a3e Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Tue, 2 Oct 2012 00:19:43 -0400 Subject: cdec cleanup, remove bayesian stuff, parsing stuff --- gi/markov_al/Makefile.am | 6 - gi/markov_al/README | 2 - gi/markov_al/ml.cc | 470 ----------------------------------------------- 3 files changed, 478 deletions(-) delete mode 100644 gi/markov_al/Makefile.am delete mode 100644 gi/markov_al/README delete mode 100644 gi/markov_al/ml.cc (limited to 'gi/markov_al') diff --git a/gi/markov_al/Makefile.am b/gi/markov_al/Makefile.am deleted file mode 100644 index fe3e3349..00000000 --- a/gi/markov_al/Makefile.am +++ /dev/null @@ -1,6 +0,0 @@ -bin_PROGRAMS = ml - -ml_SOURCES = ml.cc - -AM_CPPFLAGS = -W -Wall -Wno-sign-compare -funroll-loops -I$(top_srcdir)/utils $(GTEST_CPPFLAGS) -I$(top_srcdir)/decoder -AM_LDFLAGS = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz diff --git a/gi/markov_al/README b/gi/markov_al/README deleted file mode 100644 index 9c10f7cd..00000000 --- a/gi/markov_al/README +++ /dev/null @@ -1,2 +0,0 @@ -Experimental translation models with Markovian dependencies. - diff --git a/gi/markov_al/ml.cc b/gi/markov_al/ml.cc deleted file mode 100644 index 1e71edd6..00000000 --- a/gi/markov_al/ml.cc +++ /dev/null @@ -1,470 +0,0 @@ -#include -#include - -#include -#include -#include -#include - -#include "tdict.h" -#include "filelib.h" -#include "sampler.h" -#include "ccrp_onetable.h" -#include "array2d.h" - -using namespace std; -using namespace std::tr1; -namespace po = boost::program_options; - -void PrintTopCustomers(const CCRP_OneTable& crp) { - for (CCRP_OneTable::const_iterator it = crp.begin(); it != crp.end(); ++it) { - cerr << " " << TD::Convert(it->first) << " = " << it->second << endl; - } -} - -void PrintAlignment(const vector& src, const vector& trg, const vector& a) { - cerr << TD::GetString(src) << endl << TD::GetString(trg) << endl; - Array2D al(src.size(), trg.size()); - for (int i = 0; i < a.size(); ++i) - if (a[i] != 255) al(a[i], i) = true; - cerr << al << endl; -} - -void InitCommandLine(int argc, char** argv, po::variables_map* conf) { - po::options_description opts("Configuration options"); - opts.add_options() - ("samples,s",po::value()->default_value(1000),"Number of samples") - ("input,i",po::value(),"Read parallel data from") - ("random_seed,S",po::value(), "Random seed"); - po::options_description clo("Command line options"); - clo.add_options() - ("config", po::value(), "Configuration file") - ("help,h", "Print this help message and exit"); - 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")) { - ifstream config((*conf)["config"].as().c_str()); - po::store(po::parse_config_file(config, dconfig_options), *conf); - } - po::notify(*conf); - - if (conf->count("help") || (conf->count("input") == 0)) { - cerr << dcmdline_options << endl; - exit(1); - } -} - -struct Unigram; -struct Bigram { - Bigram() : trg(), cond() {} - Bigram(WordID prev, WordID cur, WordID t) : trg(t) { cond.first = prev; cond.second = cur; } - const pair& ConditioningPair() const { - return cond; - } - WordID& prev_src() { return cond.first; } - WordID& cur_src() { return cond.second; } - const WordID& prev_src() const { return cond.first; } - const WordID& cur_src() const { return cond.second; } - WordID trg; - private: - pair cond; -}; - -struct Unigram { - Unigram() : cur_src(), trg() {} - Unigram(WordID s, WordID t) : cur_src(s), trg(t) {} - WordID cur_src; - WordID trg; -}; - -ostream& operator<<(ostream& os, const Bigram& b) { - os << "( " << TD::Convert(b.trg) << " | " << TD::Convert(b.prev_src()) << " , " << TD::Convert(b.cur_src()) << " )"; - return os; -} - -ostream& operator<<(ostream& os, const Unigram& u) { - os << "( " << TD::Convert(u.trg) << " | " << TD::Convert(u.cur_src) << " )"; - return os; -} - -bool operator==(const Bigram& a, const Bigram& b) { - return a.trg == b.trg && a.cur_src() == b.cur_src() && a.prev_src() == b.prev_src(); -} - -bool operator==(const Unigram& a, const Unigram& b) { - return a.trg == b.trg && a.cur_src == b.cur_src; -} - -size_t hash_value(const Bigram& b) { - size_t h = boost::hash_value(b.prev_src()); - boost::hash_combine(h, boost::hash_value(b.cur_src())); - boost::hash_combine(h, boost::hash_value(b.trg)); - return h; -} - -size_t hash_value(const Unigram& u) { - size_t h = boost::hash_value(u.cur_src); - boost::hash_combine(h, boost::hash_value(u.trg)); - return h; -} - -void ReadParallelCorpus(const string& filename, - vector >* f, - vector >* e, - set* vocab_f, - set* 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 tmp; - while(*in) { - getline(*in, line); - if (line.empty() && !*in) break; - e->push_back(vector()); - f->push_back(vector()); - vector& le = e->back(); - vector& 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 UnigramModel { - UnigramModel(size_t src_voc_size, size_t trg_voc_size) : - unigrams(TD::NumWords() + 1, CCRP_OneTable(1,1,1,1)), - p0(1.0 / trg_voc_size) {} - - void increment(const Bigram& b) { - unigrams[b.cur_src()].increment(b.trg); - } - - void decrement(const Bigram& b) { - unigrams[b.cur_src()].decrement(b.trg); - } - - double prob(const Bigram& b) const { - const double q0 = unigrams[b.cur_src()].prob(b.trg, p0); - return q0; - } - - double LogLikelihood() const { - double llh = 0; - for (unsigned i = 0; i < unigrams.size(); ++i) { - const CCRP_OneTable& crp = unigrams[i]; - if (crp.num_customers() > 0) { - llh += crp.log_crp_prob(); - llh += crp.num_tables() * log(p0); - } - } - return llh; - } - - void ResampleHyperparameters(MT19937* rng) { - for (unsigned i = 0; i < unigrams.size(); ++i) - unigrams[i].resample_hyperparameters(rng); - } - - vector > unigrams; // unigrams[src].prob(trg, p0) = p(trg|src) - - const double p0; -}; - -struct BigramModel { - BigramModel(size_t src_voc_size, size_t trg_voc_size) : - unigrams(TD::NumWords() + 1, CCRP_OneTable(1,1,1,1)), - p0(1.0 / trg_voc_size) {} - - void increment(const Bigram& b) { - BigramMap::iterator it = bigrams.find(b.ConditioningPair()); - if (it == bigrams.end()) { - it = bigrams.insert(make_pair(b.ConditioningPair(), CCRP_OneTable(1,1,1,1))).first; - } - if (it->second.increment(b.trg)) - unigrams[b.cur_src()].increment(b.trg); - } - - void decrement(const Bigram& b) { - BigramMap::iterator it = bigrams.find(b.ConditioningPair()); - assert(it != bigrams.end()); - if (it->second.decrement(b.trg)) { - unigrams[b.cur_src()].decrement(b.trg); - if (it->second.num_customers() == 0) - bigrams.erase(it); - } - } - - double prob(const Bigram& b) const { - const double q0 = unigrams[b.cur_src()].prob(b.trg, p0); - const BigramMap::const_iterator it = bigrams.find(b.ConditioningPair()); - if (it == bigrams.end()) return q0; - return it->second.prob(b.trg, q0); - } - - double LogLikelihood() const { - double llh = 0; - for (unsigned i = 0; i < unigrams.size(); ++i) { - const CCRP_OneTable& crp = unigrams[i]; - if (crp.num_customers() > 0) { - llh += crp.log_crp_prob(); - llh += crp.num_tables() * log(p0); - } - } - for (BigramMap::const_iterator it = bigrams.begin(); it != bigrams.end(); ++it) { - const CCRP_OneTable& crp = it->second; - const WordID cur_src = it->first.second; - llh += crp.log_crp_prob(); - for (CCRP_OneTable::const_iterator bit = crp.begin(); bit != crp.end(); ++bit) { - llh += log(unigrams[cur_src].prob(bit->second, p0)); - } - } - return llh; - } - - void ResampleHyperparameters(MT19937* rng) { - for (unsigned i = 0; i < unigrams.size(); ++i) - unigrams[i].resample_hyperparameters(rng); - for (BigramMap::iterator it = bigrams.begin(); it != bigrams.end(); ++it) - it->second.resample_hyperparameters(rng); - } - - typedef unordered_map, CCRP_OneTable, boost::hash > > BigramMap; - BigramMap bigrams; // bigrams[(src-1,src)].prob(trg, q0) = p(trg|src,src-1) - vector > unigrams; // unigrams[src].prob(trg, p0) = p(trg|src) - - const double p0; -}; - -struct BigramAlignmentModel { - BigramAlignmentModel(size_t src_voc_size, size_t trg_voc_size) : bigrams(TD::NumWords() + 1, CCRP_OneTable(1,1,1,1)), p0(1.0 / src_voc_size) {} - void increment(WordID prev, WordID next) { - bigrams[prev].increment(next); // hierarchy? - } - void decrement(WordID prev, WordID next) { - bigrams[prev].decrement(next); // hierarchy? - } - double prob(WordID prev, WordID next) { - return bigrams[prev].prob(next, p0); - } - double LogLikelihood() const { - double llh = 0; - for (unsigned i = 0; i < bigrams.size(); ++i) { - const CCRP_OneTable& crp = bigrams[i]; - if (crp.num_customers() > 0) { - llh += crp.log_crp_prob(); - llh += crp.num_tables() * log(p0); - } - } - return llh; - } - - vector > bigrams; // bigrams[prev].prob(next, p0) = p(next|prev) - const double p0; -}; - -struct Alignment { - vector a; -}; - -int main(int argc, char** argv) { - po::variables_map conf; - InitCommandLine(argc, argv, &conf); - const unsigned samples = conf["samples"].as(); - - boost::shared_ptr prng; - if (conf.count("random_seed")) - prng.reset(new MT19937(conf["random_seed"].as())); - else - prng.reset(new MT19937); - MT19937& rng = *prng; - - vector > corpuse, corpusf; - set vocabe, vocabf; - cerr << "Reading corpus...\n"; - ReadParallelCorpus(conf["input"].as(), &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()); - const size_t corpus_len = corpusf.size(); - const WordID kNULL = TD::Convert(""); - const WordID kBOS = TD::Convert(""); - const WordID kEOS = TD::Convert(""); - Bigram TT(kBOS, TD::Convert("我"), TD::Convert("i")); - Bigram TT2(kBOS, TD::Convert("要"), TD::Convert("i")); - - UnigramModel model(vocabf.size(), vocabe.size()); - vector alignments(corpus_len); - for (unsigned ci = 0; ci < corpus_len; ++ci) { - const vector& src = corpusf[ci]; - const vector& trg = corpuse[ci]; - vector& alg = alignments[ci].a; - alg.resize(trg.size()); - int lenp1 = src.size() + 1; - WordID prev_src = kBOS; - for (int j = 0; j < trg.size(); ++j) { - int samp = lenp1 * rng.next(); - --samp; - if (samp < 0) samp = 255; - alg[j] = samp; - WordID cur_src = (samp == 255 ? kNULL : src[alg[j]]); - Bigram b(prev_src, cur_src, trg[j]); - model.increment(b); - prev_src = cur_src; - } - Bigram b(prev_src, kEOS, kEOS); - model.increment(b); - } - cerr << "Initial LLH: " << model.LogLikelihood() << endl; - - SampleSet ss; - for (unsigned si = 0; si < 50; ++si) { - for (unsigned ci = 0; ci < corpus_len; ++ci) { - const vector& src = corpusf[ci]; - const vector& trg = corpuse[ci]; - vector& alg = alignments[ci].a; - WordID prev_src = kBOS; - for (unsigned j = 0; j < trg.size(); ++j) { - unsigned char& a_j = alg[j]; - WordID cur_e_a_j = (a_j == 255 ? kNULL : src[a_j]); - Bigram b(prev_src, cur_e_a_j, trg[j]); - //cerr << "DEC: " << b << "\t" << nextb << endl; - model.decrement(b); - ss.clear(); - for (unsigned i = 0; i <= src.size(); ++i) { - const WordID cur_src = (i ? src[i-1] : kNULL); - b.cur_src() = cur_src; - ss.add(model.prob(b)); - } - int sampled_a_j = rng.SelectSample(ss); - a_j = (sampled_a_j ? sampled_a_j - 1 : 255); - cur_e_a_j = (a_j == 255 ? kNULL : src[a_j]); - b.cur_src() = cur_e_a_j; - //cerr << "INC: " << b << "\t" << nextb << endl; - model.increment(b); - prev_src = cur_e_a_j; - } - } - cerr << '.' << flush; - if (si % 10 == 9) { - cerr << "[LLH prev=" << model.LogLikelihood(); - //model.ResampleHyperparameters(&rng); - cerr << " new=" << model.LogLikelihood() << "]\n"; - //pair xx = make_pair(kBOS, TD::Convert("我")); - //PrintTopCustomers(model.bigrams.find(xx)->second); - cerr << "p(" << TT << ") = " << model.prob(TT) << endl; - cerr << "p(" << TT2 << ") = " << model.prob(TT2) << endl; - PrintAlignment(corpusf[0], corpuse[0], alignments[0].a); - } - } - { - // MODEL 2 - BigramModel model(vocabf.size(), vocabe.size()); - BigramAlignmentModel amodel(vocabf.size(), vocabe.size()); - for (unsigned ci = 0; ci < corpus_len; ++ci) { - const vector& src = corpusf[ci]; - const vector& trg = corpuse[ci]; - vector& alg = alignments[ci].a; - WordID prev_src = kBOS; - for (int j = 0; j < trg.size(); ++j) { - WordID cur_src = (alg[j] == 255 ? kNULL : src[alg[j]]); - Bigram b(prev_src, cur_src, trg[j]); - model.increment(b); - amodel.increment(prev_src, cur_src); - prev_src = cur_src; - } - amodel.increment(prev_src, kEOS); - Bigram b(prev_src, kEOS, kEOS); - model.increment(b); - } - cerr << "Initial LLH: " << model.LogLikelihood() << " " << amodel.LogLikelihood() << endl; - - SampleSet ss; - for (unsigned si = 0; si < samples; ++si) { - for (unsigned ci = 0; ci < corpus_len; ++ci) { - const vector& src = corpusf[ci]; - const vector& trg = corpuse[ci]; - vector& alg = alignments[ci].a; - WordID prev_src = kBOS; - for (unsigned j = 0; j < trg.size(); ++j) { - unsigned char& a_j = alg[j]; - WordID cur_e_a_j = (a_j == 255 ? kNULL : src[a_j]); - Bigram b(prev_src, cur_e_a_j, trg[j]); - WordID next_src = kEOS; - WordID next_trg = kEOS; - if (j < (trg.size() - 1)) { - next_src = (alg[j+1] == 255 ? kNULL : src[alg[j + 1]]); - next_trg = trg[j + 1]; - } - Bigram nextb(cur_e_a_j, next_src, next_trg); - //cerr << "DEC: " << b << "\t" << nextb << endl; - model.decrement(b); - model.decrement(nextb); - amodel.decrement(prev_src, cur_e_a_j); - amodel.decrement(cur_e_a_j, next_src); - ss.clear(); - for (unsigned i = 0; i <= src.size(); ++i) { - const WordID cur_src = (i ? src[i-1] : kNULL); - b.cur_src() = cur_src; - ss.add(model.prob(b) * model.prob(nextb) * amodel.prob(prev_src, cur_src) * amodel.prob(cur_src, next_src)); - //cerr << log(ss[ss.size() - 1]) << "\t" << b << endl; - } - int sampled_a_j = rng.SelectSample(ss); - a_j = (sampled_a_j ? sampled_a_j - 1 : 255); - cur_e_a_j = (a_j == 255 ? kNULL : src[a_j]); - b.cur_src() = cur_e_a_j; - nextb.prev_src() = cur_e_a_j; - //cerr << "INC: " << b << "\t" << nextb << endl; - //exit(1); - model.increment(b); - model.increment(nextb); - amodel.increment(prev_src, cur_e_a_j); - amodel.increment(cur_e_a_j, next_src); - prev_src = cur_e_a_j; - } - } - cerr << '.' << flush; - if (si % 10 == 9) { - cerr << "[LLH prev=" << (model.LogLikelihood() + amodel.LogLikelihood()); - //model.ResampleHyperparameters(&rng); - cerr << " new=" << model.LogLikelihood() << "]\n"; - pair xx = make_pair(kBOS, TD::Convert("我")); - cerr << "p(" << TT << ") = " << model.prob(TT) << endl; - cerr << "p(" << TT2 << ") = " << model.prob(TT2) << endl; - pair xx2 = make_pair(kBOS, TD::Convert("要")); - PrintTopCustomers(model.bigrams.find(xx)->second); - //PrintTopCustomers(amodel.bigrams[TD::Convert("")]); - //PrintTopCustomers(model.unigrams[TD::Convert("")]); - PrintAlignment(corpusf[0], corpuse[0], alignments[0].a); - } - } - } - return 0; -} - -- cgit v1.2.3