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-rw-r--r--gi/pf/dpnaive.cc349
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diff --git a/gi/pf/dpnaive.cc b/gi/pf/dpnaive.cc
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+#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 "trule.h"
+#include "tdict.h"
+#include "filelib.h"
+#include "dict.h"
+#include "sampler.h"
+#include "ccrp_nt.h"
+
+using namespace std;
+using namespace std::tr1;
+namespace po = boost::program_options;
+
+static unsigned kMAX_SRC_PHRASE;
+static unsigned kMAX_TRG_PHRASE;
+struct FSTState;
+
+size_t hash_value(const TRule& r) {
+ size_t h = 2 - r.lhs_;
+ boost::hash_combine(h, boost::hash_value(r.e_));
+ boost::hash_combine(h, boost::hash_value(r.f_));
+ return h;
+}
+
+bool operator==(const TRule& a, const TRule& b) {
+ return (a.lhs_ == b.lhs_ && a.e_ == b.e_ && a.f_ == b.f_);
+}
+
+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);
+ }
+}
+
+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) :
+ rng(&*prng),
+ p0(b),
+ baseprob(prob_t::One()),
+ corpuse(ce),
+ 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,
+ 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);
+ //cerr << "REMOVE: " << x.AsString() << endl;
+ if (rules.decrement(x)) {
+ baseprob /= p0(x);
+ //cerr << " (REMOVED ONLY INSTANCE)\n";
+ }
+ }
+ }
+
+ 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);
+ if (rules.increment(x)) {
+ baseprob *= p0(x);
+ }
+ }
+ }
+
+ prob_t Likelihood() const {
+ prob_t p;
+ p.logeq(rules.log_crp_prob());
+ return p * baseprob;
+ }
+
+ prob_t DerivationProposalProbability(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) const {
+ prob_t p = prob_t::One();
+ if (d.size() < 2) return p;
+ TRule x;
+ 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;
+ }
+ return p;
+ }
+
+ void Sample();
+
+ 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;
+};
+
+template <typename Base>
+void ModelAndData<Base>::Sample() {
+ unsigned MAXK = 4;
+ unsigned MAXL = 4;
+ 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();
+ 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]);
+ trans[i][j][k - 1][l - 1].logeq(rules.logprob(x, log(p0(x))));
+ 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;
+ 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());
+
+ ModelAndData<PhraseJointBase> posterior(lp0, corpuse, corpusf, vocabe, vocabf);
+ posterior.Sample();
+
+ return 0;
+}
+
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