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#include <iostream>
#include <tr1/memory>
#include <queue>
#include <boost/functional.hpp>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "corpus_tools.h"
#include "m.h"
#include "tdict.h"
#include "sampler.h"
#include "ccrp.h"
#include "gamma_poisson.h"
// A not very memory-efficient implementation of an 1-gram LM based on PYPs
// as described in Y.-W. Teh. (2006) A Hierarchical Bayesian Language Model
// based on Pitman-Yor Processes. In Proc. ACL.
using namespace std;
using namespace tr1;
namespace po = boost::program_options;
boost::shared_ptr<MT19937> prng;
void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("samples,n",po::value<unsigned>()->default_value(50),"Number of samples")
("train,i",po::value<string>(),"Training data file")
("test,T",po::value<string>(),"Test data file")
("discount_prior_a,a",po::value<double>()->default_value(1.0), "discount ~ Beta(a,b): a=this")
("discount_prior_b,b",po::value<double>()->default_value(1.0), "discount ~ Beta(a,b): b=this")
("strength_prior_s,s",po::value<double>()->default_value(1.0), "strength ~ Gamma(s,r): s=this")
("strength_prior_r,r",po::value<double>()->default_value(1.0), "strength ~ Gamma(s,r): r=this")
("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", "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("train") == 0)) {
cerr << dcmdline_options << endl;
exit(1);
}
}
struct Histogram {
void increment(unsigned bin, unsigned delta = 1u) {
data[bin] += delta;
}
void decrement(unsigned bin, unsigned delta = 1u) {
data[bin] -= delta;
}
void move(unsigned from_bin, unsigned to_bin, unsigned delta = 1u) {
decrement(from_bin, delta);
increment(to_bin, delta);
}
map<unsigned, unsigned> data;
// SparseVector<unsigned> data;
};
// Lord Rothschild. 1986. THE DISTRIBUTION OF ENGLISH DICTIONARY WORD LENGTHS.
// Journal of Statistical Planning and Inference 14 (1986) 311-322
struct PoissonLengthUniformCharWordModel {
explicit PoissonLengthUniformCharWordModel(unsigned vocab_size) : plen(5,5), uc(-log(50)), llh() {}
void increment(WordID w, MT19937*) {
llh += log(prob(w)); // this isn't quite right
plen.increment(TD::Convert(w).size() - 1);
}
void decrement(WordID w, MT19937*) {
plen.decrement(TD::Convert(w).size() - 1);
llh -= log(prob(w)); // this isn't quite right
}
double prob(WordID w) const {
size_t len = TD::Convert(w).size();
return plen.prob(len - 1) * exp(uc * len);
}
double log_likelihood() const { return llh; }
void resample_hyperparameters(MT19937*) {}
GammaPoisson plen;
const double uc;
double llh;
};
// uniform base distribution (0-gram model)
struct UniformWordModel {
explicit UniformWordModel(unsigned vocab_size) : p0(1.0 / vocab_size), draws() {}
void increment(WordID, MT19937*) { ++draws; }
void decrement(WordID, MT19937*) { --draws; assert(draws >= 0); }
double prob(WordID) const { return p0; } // all words have equal prob
double log_likelihood() const { return draws * log(p0); }
void resample_hyperparameters(MT19937*) {}
const double p0;
int draws;
};
// represents an Unigram LM
template <class BaseGenerator>
struct UnigramLM {
UnigramLM(unsigned vs, double da, double db, double ss, double sr) :
base(vs),
crp(da, db, ss, sr, 0.8, 1.0) {}
void increment(WordID w, MT19937* rng) {
const double backoff = base.prob(w);
if (crp.increment(w, backoff, rng))
base.increment(w, rng);
}
void decrement(WordID w, MT19937* rng) {
if (crp.decrement(w, rng))
base.decrement(w, rng);
}
double prob(WordID w) const {
const double backoff = base.prob(w);
return crp.prob(w, backoff);
}
double log_likelihood() const {
double llh = base.log_likelihood();
llh += crp.log_crp_prob();
return llh;
}
void resample_hyperparameters(MT19937* rng) {
crp.resample_hyperparameters(rng);
base.resample_hyperparameters(rng);
}
double discount_a, discount_b, strength_s, strength_r;
double d, strength;
BaseGenerator base;
CCRP<WordID> crp;
};
int main(int argc, char** argv) {
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
const unsigned samples = conf["samples"].as<unsigned>();
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<WordID> > corpuse;
set<WordID> vocabe;
const WordID kEOS = TD::Convert("</s>");
cerr << "Reading corpus...\n";
CorpusTools::ReadFromFile(conf["train"].as<string>(), &corpuse, &vocabe);
cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
vector<vector<WordID> > test;
if (conf.count("test"))
CorpusTools::ReadFromFile(conf["test"].as<string>(), &test);
else
test = corpuse;
#if 1
UnigramLM<PoissonLengthUniformCharWordModel> lm(vocabe.size(),
#else
UnigramLM<UniformWordModel> lm(vocabe.size(),
#endif
conf["discount_prior_a"].as<double>(),
conf["discount_prior_b"].as<double>(),
conf["strength_prior_s"].as<double>(),
conf["strength_prior_r"].as<double>());
for (unsigned SS=0; SS < samples; ++SS) {
for (unsigned ci = 0; ci < corpuse.size(); ++ci) {
const vector<WordID>& s = corpuse[ci];
for (unsigned i = 0; i <= s.size(); ++i) {
WordID w = (i < s.size() ? s[i] : kEOS);
if (SS > 0) lm.decrement(w, &rng);
lm.increment(w, &rng);
}
if (SS > 0) lm.decrement(kEOS, &rng);
lm.increment(kEOS, &rng);
}
cerr << "LLH=" << lm.log_likelihood() << "\t tables=" << lm.crp.num_tables() << " " << endl;
if (SS % 10 == 9) lm.resample_hyperparameters(&rng);
}
double llh = 0;
unsigned cnt = 0;
unsigned oovs = 0;
for (unsigned ci = 0; ci < test.size(); ++ci) {
const vector<WordID>& s = test[ci];
for (unsigned i = 0; i <= s.size(); ++i) {
WordID w = (i < s.size() ? s[i] : kEOS);
double lp = log(lm.prob(w)) / log(2);
if (i < s.size() && vocabe.count(w) == 0) {
cerr << "**OOV ";
++oovs;
//lp = 0;
}
cerr << "p(" << TD::Convert(w) << ") = " << lp << endl;
llh -= lp;
cnt++;
}
}
cerr << " Log_10 prob: " << (-llh * log(2) / log(10)) << endl;
cerr << " Count: " << cnt << endl;
cerr << " OOVs: " << oovs << endl;
cerr << "Cross-entropy: " << (llh / cnt) << endl;
cerr << " Perplexity: " << pow(2, llh / cnt) << endl;
return 0;
}
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