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#include <iostream>
#include <queue>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "tdict.h"
#include "stringlib.h"
#include "filelib.h"
#include "array2d.h"
#include "sampler.h"
#include "corpus.h"
#include "pyp_tm.h"
#include "hpyp_tm.h"
#include "quasi_model2.h"
using namespace std;
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")
("infer_alignment_hyperparameters,I", "Infer alpha and p_null, otherwise fixed values will be assumed")
("p_null,0", po::value<double>()->default_value(0.08), "probability of aligning to null")
("align_alpha,a", po::value<double>()->default_value(4.0), "how 'tight' is the bias toward be along the diagonal?")
("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);
}
}
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;
};
template <class LexicalTranslationModel>
struct Aligner {
Aligner(const vector<vector<WordID> >& lets,
int vocab_size,
int num_letters,
const po::variables_map& conf,
vector<AlignedSentencePair>* c) :
corpus(*c),
paj_model(conf["align_alpha"].as<double>(), conf["p_null"].as<double>()),
infer_paj(conf.count("infer_alignment_hyperparameters") > 0),
model(lets, vocab_size, num_letters),
kNULL(TD::Convert("NULL")) {
assert(lets[kNULL].size() == 0);
}
vector<AlignedSentencePair>& corpus;
QuasiModel2 paj_model;
const bool infer_paj;
LexicalTranslationModel model;
const WordID kNULL;
void ResampleHyperparameters() {
model.ResampleHyperparameters(prng);
if (infer_paj) paj_model.ResampleHyperparameters(prng);
}
void InitializeRandom() {
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) {
unsigned char& a_j = asp.a[j].src_index;
a_j = prng->next() * (1 + asp.src.size());
const WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
model.Increment(f_a_j, asp.trg[j], &*prng);
paj_model.Increment(a_j, j, asp.src.size(), asp.trg.size());
}
}
cerr << "Corpus intialized randomly." << endl;
cerr << "LLH = " << Likelihood() << " \t(Amodel=" << paj_model.Likelihood()
<< " TModel=" << model.Likelihood() << ") contexts=" << model.UniqueConditioningContexts() << endl;
}
void ResampleCorpus() {
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) {
unsigned char& a_j = asp.a[j].src_index;
const WordID e_j = asp.trg[j];
WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
model.Decrement(f_a_j, e_j, prng);
paj_model.Decrement(a_j, j, asp.src.size(), asp.trg.size());
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);
ss[prop_a_j] = model.Prob(prop_f, e_j);
ss[prop_a_j] *= paj_model.Prob(prop_a_j, j, asp.src.size(), asp.trg.size());
}
a_j = prng->SelectSample(ss);
f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
model.Increment(f_a_j, e_j, prng);
paj_model.Increment(a_j, j, asp.src.size(), asp.trg.size());
}
}
}
prob_t Likelihood() const {
return model.Likelihood() * paj_model.Likelihood();
}
};
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) {
assert(asp.a[j].src_index <= asp.src.size());
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 = new MT19937(conf["random_seed"].as<uint32_t>());
else
prng = new MT19937;
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();
//Aligner<PYPLexicalTranslation> aligner(letters, vocabe.size(), letset.size(), conf, &corpus);
Aligner<HPYPLexicalTranslation> aligner(letters, vocabe.size(), letset.size(), conf, &corpus);
aligner.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]);
if (i % 10 == 9) {
aligner.ResampleHyperparameters();
cerr << "LLH = " << aligner.Likelihood() << " \t(Amodel=" << aligner.paj_model.Likelihood()
<< " TModel=" << aligner.model.Likelihood() << ") contexts=" << aligner.model.UniqueConditioningContexts() << endl;
}
aligner.ResampleCorpus();
if (i > (samples / 5) && (i % 6 == 5)) for (int j = 0; j < corpus.size(); ++j) AddSample(&corpus[j]);
}
for (unsigned i = 0; i < corpus.size(); ++i)
WriteAlignments(corpus[i]);
aligner.model.Summary();
return 0;
}
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