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-rw-r--r--gi/pf/align-lexonly-pyp.cc327
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diff --git a/gi/pf/align-lexonly-pyp.cc b/gi/pf/align-lexonly-pyp.cc
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+++ b/gi/pf/align-lexonly-pyp.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 "array2d.h"
+#include "base_measures.h"
+#include "monotonic_pseg.h"
+#include "conditional_pseg.h"
+#include "trule.h"
+#include "tdict.h"
+#include "stringlib.h"
+#include "filelib.h"
+#include "dict.h"
+#include "sampler.h"
+#include "mfcr.h"
+#include "corpus.h"
+#include "ngram_base.h"
+
+using namespace std;
+using namespace tr1;
+namespace po = boost::program_options;
+
+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")
+ ("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;
+
+struct LexicalAlignment {
+ unsigned char src_index;
+ bool is_transliteration;
+ vector<pair<short, short> > derivation;
+};
+
+struct AlignedSentencePair {
+ vector<WordID> src;
+ vector<WordID> trg;
+ vector<LexicalAlignment> a;
+ Array2D<short> posterior;
+};
+
+struct HierarchicalWordBase {
+ explicit HierarchicalWordBase(const unsigned vocab_e_size) :
+ base(prob_t::One()), r(1,1,1,25,25), u0(-log(vocab_e_size)), l(1,1.0), v(1, 0.0) {}
+
+ void ResampleHyperparameters(MT19937* rng) {
+ r.resample_hyperparameters(rng);
+ }
+
+ inline double logp0(const vector<WordID>& s) const {
+ return s.size() * u0;
+ }
+
+ // return p0 of rule.e_
+ prob_t operator()(const TRule& rule) const {
+ v[0] = exp(logp0(rule.e_));
+ return prob_t(r.prob(rule.e_, v, l));
+ }
+
+ void Increment(const TRule& rule) {
+ v[0] = exp(logp0(rule.e_));
+ if (r.increment(rule.e_, v, l, &*prng).count) {
+ base *= prob_t(v[0] * l[0]);
+ }
+ }
+
+ void Decrement(const TRule& rule) {
+ if (r.decrement(rule.e_, &*prng).count) {
+ base /= prob_t(exp(logp0(rule.e_)));
+ }
+ }
+
+ prob_t Likelihood() const {
+ prob_t p; p.logeq(r.log_crp_prob());
+ p *= base;
+ return p;
+ }
+
+ void Summary() const {
+ cerr << "NUMBER OF CUSTOMERS: " << r.num_customers() << " (d=" << r.d() << ",\\alpha=" << r.alpha() << ')' << endl;
+ for (MFCR<vector<WordID> >::const_iterator it = r.begin(); it != r.end(); ++it)
+ cerr << " " << it->second.total_dish_count_ << " (on " << it->second.table_counts_.size() << " tables)" << TD::GetString(it->first) << endl;
+ }
+
+ prob_t base;
+ MFCR<vector<WordID> > r;
+ const double u0;
+ const vector<double> l;
+ mutable vector<double> v;
+};
+
+struct BasicLexicalAlignment {
+ explicit BasicLexicalAlignment(const vector<vector<WordID> >& lets,
+ const unsigned words_e,
+ const unsigned letters_e,
+ vector<AlignedSentencePair>* corp) :
+ letters(lets),
+ corpus(*corp),
+ //up0(words_e),
+ //up0("en.chars.1gram", letters_e),
+ //up0("en.words.1gram"),
+ up0(letters_e),
+ //up0("en.chars.2gram"),
+ tmodel(up0) {
+ }
+
+ void InstantiateRule(const WordID src,
+ const WordID trg,
+ TRule* rule) const {
+ static const WordID kX = TD::Convert("X") * -1;
+ rule->lhs_ = kX;
+ rule->e_ = letters[trg];
+ rule->f_ = letters[src];
+ }
+
+ void InitializeRandom() {
+ const WordID kNULL = TD::Convert("NULL");
+ cerr << "Initializing with random alignments ...\n";
+ for (unsigned i = 0; i < corpus.size(); ++i) {
+ AlignedSentencePair& asp = corpus[i];
+ asp.a.resize(asp.trg.size());
+ for (unsigned j = 0; j < asp.trg.size(); ++j) {
+ const unsigned char a_j = prng->next() * (1 + asp.src.size());
+ const WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
+ TRule r;
+ InstantiateRule(f_a_j, asp.trg[j], &r);
+ asp.a[j].is_transliteration = false;
+ asp.a[j].src_index = a_j;
+ if (tmodel.IncrementRule(r, &*prng))
+ up0.Increment(r);
+ }
+ }
+ cerr << " LLH = " << Likelihood() << endl;
+ }
+
+ prob_t Likelihood() const {
+ prob_t p = tmodel.Likelihood();
+ p *= up0.Likelihood();
+ return p;
+ }
+
+ void ResampleHyperparemeters() {
+ cerr << " LLH_prev = " << Likelihood() << flush;
+ tmodel.ResampleHyperparameters(&*prng);
+ up0.ResampleHyperparameters(&*prng);
+ cerr << "\tLLH_post = " << Likelihood() << endl;
+ }
+
+ void ResampleCorpus();
+
+ const vector<vector<WordID> >& letters; // spelling dictionary
+ vector<AlignedSentencePair>& corpus;
+ //PhraseConditionalUninformativeBase up0;
+ //PhraseConditionalUninformativeUnigramBase up0;
+ //UnigramWordBase up0;
+ //HierarchicalUnigramBase up0;
+ HierarchicalWordBase up0;
+ //CompletelyUniformBase up0;
+ //FixedNgramBase up0;
+ //ConditionalTranslationModel<PhraseConditionalUninformativeBase> tmodel;
+ //ConditionalTranslationModel<PhraseConditionalUninformativeUnigramBase> tmodel;
+ //ConditionalTranslationModel<UnigramWordBase> tmodel;
+ //ConditionalTranslationModel<HierarchicalUnigramBase> tmodel;
+ MConditionalTranslationModel<HierarchicalWordBase> tmodel;
+ //ConditionalTranslationModel<FixedNgramBase> tmodel;
+ //ConditionalTranslationModel<CompletelyUniformBase> tmodel;
+};
+
+void BasicLexicalAlignment::ResampleCorpus() {
+ static const WordID kNULL = TD::Convert("NULL");
+ for (unsigned i = 0; i < corpus.size(); ++i) {
+ AlignedSentencePair& asp = corpus[i];
+ SampleSet<prob_t> ss; ss.resize(asp.src.size() + 1);
+ for (unsigned j = 0; j < asp.trg.size(); ++j) {
+ TRule r;
+ unsigned char& a_j = asp.a[j].src_index;
+ WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
+ InstantiateRule(f_a_j, asp.trg[j], &r);
+ if (tmodel.DecrementRule(r, &*prng))
+ up0.Decrement(r);
+
+ for (unsigned prop_a_j = 0; prop_a_j <= asp.src.size(); ++prop_a_j) {
+ const WordID prop_f = (prop_a_j ? asp.src[prop_a_j - 1] : kNULL);
+ InstantiateRule(prop_f, asp.trg[j], &r);
+ ss[prop_a_j] = tmodel.RuleProbability(r);
+ }
+ a_j = prng->SelectSample(ss);
+ f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
+ InstantiateRule(f_a_j, asp.trg[j], &r);
+ if (tmodel.IncrementRule(r, &*prng))
+ up0.Increment(r);
+ }
+ }
+ cerr << " LLH = " << tmodel.Likelihood() << endl;
+}
+
+void ExtractLetters(const set<WordID>& v, vector<vector<WordID> >* l, set<WordID>* letset = NULL) {
+ for (set<WordID>::const_iterator it = v.begin(); it != v.end(); ++it) {
+ vector<WordID>& letters = (*l)[*it];
+ if (letters.size()) continue; // if e and f have the same word
+
+ const string& w = TD::Convert(*it);
+
+ size_t cur = 0;
+ while (cur < w.size()) {
+ const size_t len = UTF8Len(w[cur]);
+ letters.push_back(TD::Convert(w.substr(cur, len)));
+ if (letset) letset->insert(letters.back());
+ cur += len;
+ }
+ }
+}
+
+void Debug(const AlignedSentencePair& asp) {
+ cerr << TD::GetString(asp.src) << endl << TD::GetString(asp.trg) << endl;
+ Array2D<bool> a(asp.src.size(), asp.trg.size());
+ for (unsigned j = 0; j < asp.trg.size(); ++j)
+ if (asp.a[j].src_index) a(asp.a[j].src_index - 1, j) = true;
+ cerr << a << endl;
+}
+
+void AddSample(AlignedSentencePair* asp) {
+ for (unsigned j = 0; j < asp->trg.size(); ++j)
+ asp->posterior(asp->a[j].src_index, j)++;
+}
+
+void WriteAlignments(const AlignedSentencePair& asp) {
+ bool first = true;
+ for (unsigned j = 0; j < asp.trg.size(); ++j) {
+ int src_index = -1;
+ int mc = -1;
+ for (unsigned i = 0; i <= asp.src.size(); ++i) {
+ if (asp.posterior(i, j) > mc) {
+ mc = asp.posterior(i, j);
+ src_index = i;
+ }
+ }
+
+ if (src_index) {
+ if (first) first = false; else cout << ' ';
+ cout << (src_index - 1) << '-' << j;
+ }
+ }
+ cout << endl;
+}
+
+int main(int argc, char** argv) {
+ po::variables_map conf;
+ InitCommandLine(argc, argv, &conf);
+
+ 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());
+
+ vector<AlignedSentencePair> corpus(corpuse.size());
+ for (unsigned i = 0; i < corpuse.size(); ++i) {
+ corpus[i].src.swap(corpusf[i]);
+ corpus[i].trg.swap(corpuse[i]);
+ corpus[i].posterior.resize(corpus[i].src.size() + 1, corpus[i].trg.size());
+ }
+ corpusf.clear(); corpuse.clear();
+
+ vocabf.insert(TD::Convert("NULL"));
+ vector<vector<WordID> > letters(TD::NumWords());
+ set<WordID> letset;
+ ExtractLetters(vocabe, &letters, &letset);
+ ExtractLetters(vocabf, &letters, NULL);
+ letters[TD::Convert("NULL")].clear();
+
+ BasicLexicalAlignment x(letters, vocabe.size(), letset.size(), &corpus);
+ x.InitializeRandom();
+ const unsigned samples = conf["samples"].as<unsigned>();
+ for (int i = 0; i < samples; ++i) {
+ for (int j = 65; j < 67; ++j) Debug(corpus[j]);
+ cerr << i << "\t" << x.tmodel.r.size() << "\t";
+ if (i % 10 == 0) x.ResampleHyperparemeters();
+ x.ResampleCorpus();
+ if (i > (samples / 5) && (i % 10 == 9)) for (int j = 0; j < corpus.size(); ++j) AddSample(&corpus[j]);
+ }
+ for (unsigned i = 0; i < corpus.size(); ++i)
+ WriteAlignments(corpus[i]);
+ //ModelAndData posterior(x, &corpus, vocabe, vocabf);
+ x.tmodel.Summary();
+ x.up0.Summary();
+
+ //posterior.Sample();
+
+ return 0;
+}
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