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Diffstat (limited to 'utils/unigram_pyp_lm.cc')
| -rw-r--r-- | utils/unigram_pyp_lm.cc | 168 |
1 files changed, 0 insertions, 168 deletions
diff --git a/utils/unigram_pyp_lm.cc b/utils/unigram_pyp_lm.cc deleted file mode 100644 index 510e8839..00000000 --- a/utils/unigram_pyp_lm.cc +++ /dev/null @@ -1,168 +0,0 @@ -#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" - -// 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); - } -} - -// uniform base distribution (0-gram model) -struct UniformWordModel { - explicit UniformWordModel(unsigned vocab_size) : p0(1.0 / vocab_size), draws() {} - void increment() { ++draws; } - void decrement() { --draws; assert(draws >= 0); } - double prob(WordID) const { return p0; } // all words have equal prob - double log_likelihood() const { return draws * log(p0); } - const double p0; - int draws; -}; - -// represents an Unigram LM -struct UnigramLM { - UnigramLM(unsigned vs, double da, double db, double ss, double sr) : - uniform_vocab(vs), - crp(da, db, ss, sr, 0.8, 1.0) {} - void increment(WordID w, MT19937* rng) { - const double backoff = uniform_vocab.prob(w); - if (crp.increment(w, backoff, rng)) - uniform_vocab.increment(); - } - void decrement(WordID w, MT19937* rng) { - if (crp.decrement(w, rng)) - uniform_vocab.decrement(); - } - double prob(WordID w) const { - const double backoff = uniform_vocab.prob(w); - return crp.prob(w, backoff); - } - - double log_likelihood() const { - double llh = uniform_vocab.log_likelihood(); - llh += crp.log_crp_prob(); - return llh; - } - - void resample_hyperparameters(MT19937* rng) { - crp.resample_hyperparameters(rng); - } - - double discount_a, discount_b, strength_s, strength_r; - double d, strength; - UniformWordModel uniform_vocab; - 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; - UnigramLM lm(vocabe.size(), - 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 (int SS=0; SS < samples; ++SS) { - for (int ci = 0; ci < corpuse.size(); ++ci) { - const vector<WordID>& s = corpuse[ci]; - for (int 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() << endl; - //if (SS % 10 == 9) lm.resample_hyperparameters(&rng); - } - double llh = 0; - unsigned cnt = 0; - unsigned oovs = 0; - for (int ci = 0; ci < test.size(); ++ci) { - const vector<WordID>& s = test[ci]; - for (int 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; -} - |