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Diffstat (limited to 'gi/pf/dpnaive.cc')
| -rw-r--r-- | gi/pf/dpnaive.cc | 294 |
1 files changed, 294 insertions, 0 deletions
diff --git a/gi/pf/dpnaive.cc b/gi/pf/dpnaive.cc new file mode 100644 index 00000000..c926487b --- /dev/null +++ b/gi/pf/dpnaive.cc @@ -0,0 +1,294 @@ +#include <iostream> +#include <tr1/memory> +#include <queue> + +#include <boost/multi_array.hpp> +#include <boost/program_options.hpp> +#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; +namespace po = boost::program_options; + +static unsigned kMAX_SRC_PHRASE; +static unsigned kMAX_TRG_PHRASE; + +void InitCommandLine(int argc, char** argv, po::variables_map* conf) { + po::options_description opts("Configuration options"); + opts.add_options() + ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples") + ("input,i",po::value<string>(),"Read parallel data from") + ("max_src_phrase",po::value<unsigned>()->default_value(4),"Maximum length of source language phrases") + ("max_trg_phrase",po::value<unsigned>()->default_value(4),"Maximum length of target language phrases") + ("model1,m",po::value<string>(),"Model 1 parameters (used in base distribution)") + ("model1_interpolation_weight",po::value<double>()->default_value(0.95),"Mixing proportion of model 1 with uniform target distribution") + ("random_seed,S",po::value<uint32_t>(), "Random seed"); + po::options_description clo("Command line options"); + clo.add_options() + ("config", po::value<string>(), "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<string>().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); + } +} + +shared_ptr<MT19937> prng; + +template <typename Base> +struct ModelAndData { + 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()), + corpuse(ce), + corpusf(cf), + vocabe(ve), + vocabf(vf), + mh_samples(), + mh_rejects(), + kX(-TD::Convert("X")), + derivations(corpuse.size()) {} + + void ResampleHyperparameters() { + } + + void InstantiateRule(const pair<short,short>& from, + const pair<short,short>& to, + const vector<int>& sentf, + const vector<int>& sente, + TRule* rule) const { + rule->f_.clear(); + rule->e_.clear(); + rule->lhs_ = kX; + for (short i = from.first; i < to.first; ++i) + rule->f_.push_back(sentf[i]); + for (short i = from.second; i < to.second; ++i) + rule->e_.push_back(sente[i]); + } + + void DecrementDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) { + if (d.size() < 2) return; + TRule x; + for (int i = 1; i < d.size(); ++i) { + InstantiateRule(d[i], d[i-1], sentf, sente, &x); + model.DecrementRule(x); + model.DecrementContinue(); + } + model.DecrementStop(); + } + + void PrintDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) { + if (d.size() < 2) return; + TRule x; + for (int i = 1; i < d.size(); ++i) { + InstantiateRule(d[i], d[i-1], sentf, sente, &x); + cerr << i << '/' << (d.size() - 1) << ": " << x << endl; + } + } + + void IncrementDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) { + if (d.size() < 2) return; + TRule x; + for (int i = 1; i < d.size(); ++i) { + InstantiateRule(d[i], d[i-1], sentf, sente, &x); + model.IncrementRule(x); + model.IncrementContinue(); + } + model.IncrementStop(); + } + + prob_t Likelihood() const { + return model.Likelihood(); + } + + prob_t DerivationProposalProbability(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) const { + 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); + 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; + unsigned mh_samples, mh_rejects; + const int kX; + vector<vector<pair<short, short> > > derivations; +}; + +template <typename Base> +void ModelAndData<Base>::Sample() { + unsigned MAXK = kMAX_SRC_PHRASE; + unsigned MAXL = kMAX_TRG_PHRASE; + TRule x; + x.lhs_ = -TD::Convert("X"); + for (int samples = 0; samples < 1000; ++samples) { + if (samples % 1 == 0 && samples > 0) { + //ResampleHyperparameters(); + cerr << " [" << samples << " LLH=" << log(Likelihood()) << " MH=" << ((double)mh_rejects / mh_samples) << "]\n"; + for (int i = 0; i < 10; ++i) { + cerr << "SENTENCE: " << TD::GetString(corpusf[i]) << " ||| " << TD::GetString(corpuse[i]) << endl; + PrintDerivation(derivations[i], corpusf[i], corpuse[i]); + } + } + cerr << '.' << flush; + for (int s = 0; s < corpuse.size(); ++s) { + const vector<int>& sentf = corpusf[s]; + const vector<int>& sente = corpuse[s]; +// cerr << " CUSTOMERS: " << rules.num_customers() << endl; +// cerr << "SENTENCE: " << TD::GetString(sentf) << " ||| " << TD::GetString(sente) << endl; + + vector<pair<short, short> >& deriv = derivations[s]; + const prob_t p_cur = Likelihood(); + DecrementDerivation(deriv, sentf, sente); + + 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]; + x.f_.clear(); + for (int k = 1; k <= MAXK; ++k) { + if (i + k > sentf.size()) break; + x.f_.push_back(sentf[i + k - 1]); + x.e_.clear(); + for (int l = 1; l <= MAXL; ++l) { + if (j + l > sente.size()) break; + x.e_.push_back(sente[j + l - 1]); + 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]; + } + } + } + } +// cerr << "Inside: " << log(a[sentf.size()][sente.size()]) << endl; + const prob_t q_cur = DerivationProposalProbability(deriv, sentf, sente); + + vector<pair<short,short> > newderiv; + int cur_i = sentf.size(); + int cur_j = sente.size(); + while(cur_i > 0 && cur_j > 0) { + newderiv.push_back(pair<short,short>(cur_i, cur_j)); +// cerr << "NODE: (" << cur_i << "," << cur_j << ")\n"; + SampleSet<prob_t> ss; + vector<pair<short,short> > nexts; + for (int k = 1; k <= MAXK; ++k) { + const int hyp_i = cur_i - k; + if (hyp_i < 0) break; + for (int l = 1; l <= MAXL; ++l) { + const int hyp_j = cur_j - l; + if (hyp_j < 0) break; + const prob_t& inside = a[hyp_i][hyp_j]; + if (inside == prob_t::Zero()) continue; + const prob_t& transp = trans[hyp_i][hyp_j][k - 1][l - 1]; + if (transp == prob_t::Zero()) continue; + const prob_t p = inside * transp; + ss.add(p); + nexts.push_back(pair<short,short>(hyp_i, hyp_j)); +// cerr << " (" << hyp_i << "," << hyp_j << ") <--- " << log(p) << endl; + } + } +// cerr << " sample set has " << nexts.size() << " elements.\n"; + const int selected = rng->SelectSample(ss); + cur_i = nexts[selected].first; + cur_j = nexts[selected].second; + } + newderiv.push_back(pair<short,short>(0,0)); + const prob_t q_new = DerivationProposalProbability(newderiv, sentf, sente); + IncrementDerivation(newderiv, sentf, sente); +// cerr << "SANITY: " << q_new << " " <<log(DerivationProposalProbability(newderiv, sentf, sente)) << endl; + if (deriv.empty()) { deriv = newderiv; continue; } + ++mh_samples; + + if (deriv != newderiv) { + const prob_t p_new = Likelihood(); +// cerr << "p_cur=" << log(p_cur) << "\t p_new=" << log(p_new) << endl; +// cerr << "q_cur=" << log(q_cur) << "\t q_new=" << log(q_new) << endl; + if (!rng->AcceptMetropolisHastings(p_new, p_cur, q_new, q_cur)) { + ++mh_rejects; + DecrementDerivation(newderiv, sentf, sente); + IncrementDerivation(deriv, sentf, sente); + } else { +// cerr << " ACCEPT\n"; + deriv = newderiv; + } + } + } + } +} + +int main(int argc, char** argv) { + po::variables_map conf; + InitCommandLine(argc, argv, &conf); + kMAX_TRG_PHRASE = conf["max_trg_phrase"].as<unsigned>(); + kMAX_SRC_PHRASE = conf["max_src_phrase"].as<unsigned>(); + + if (!conf.count("model1")) { + cerr << argv[0] << "Please use --model1 to specify model 1 parameters\n"; + return 1; + } + if (conf.count("random_seed")) + prng.reset(new MT19937(conf["random_seed"].as<uint32_t>())); + else + prng.reset(new MT19937); +// MT19937& rng = *prng; + + vector<vector<int> > corpuse, corpusf; + set<int> vocabe, vocabf; + 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"; + cerr << "f-Vocabulary size: " << vocabe.size() << " types\n"; + assert(corpusf.size() == corpuse.size()); + + 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(m, lp0, corpuse, corpusf, vocabe, vocabf); + posterior.Sample(); + + return 0; +} + |