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-rw-r--r--gi/pf/pyp_lm.cc209
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diff --git a/gi/pf/pyp_lm.cc b/gi/pf/pyp_lm.cc
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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 "tied_resampler.h"
-
-// A not very memory-efficient implementation of an N-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.
-
-// I use templates to handle the recursive formalation of the prior, so
-// the order of the model has to be specified here, at compile time:
-#define kORDER 3
-
-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(300),"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);
- }
-}
-
-template <unsigned N> struct PYPLM;
-
-// uniform base distribution (0-gram model)
-template<> struct PYPLM<0> {
- PYPLM(unsigned vs, double, double, double, double) : p0(1.0 / vs), draws() {}
- void increment(WordID, const vector<WordID>&, MT19937*) { ++draws; }
- void decrement(WordID, const vector<WordID>&, MT19937*) { --draws; assert(draws >= 0); }
- double prob(WordID, const vector<WordID>&) const { return p0; }
- void resample_hyperparameters(MT19937*) {}
- double log_likelihood() const { return draws * log(p0); }
- const double p0;
- int draws;
-};
-
-// represents an N-gram LM
-template <unsigned N> struct PYPLM {
- PYPLM(unsigned vs, double da, double db, double ss, double sr) :
- backoff(vs, da, db, ss, sr),
- tr(da, db, ss, sr, 0.8, 1.0),
- lookup(N-1) {}
- void increment(WordID w, const vector<WordID>& context, MT19937* rng) {
- const double bo = backoff.prob(w, context);
- for (unsigned i = 0; i < N-1; ++i)
- lookup[i] = context[context.size() - 1 - i];
- typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::iterator it = p.find(lookup);
- if (it == p.end()) {
- it = p.insert(make_pair(lookup, CCRP<WordID>(0.5,1))).first;
- tr.Add(&it->second); // add to resampler
- }
- if (it->second.increment(w, bo, rng))
- backoff.increment(w, context, rng);
- }
- void decrement(WordID w, const vector<WordID>& context, MT19937* rng) {
- for (unsigned i = 0; i < N-1; ++i)
- lookup[i] = context[context.size() - 1 - i];
- typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::iterator it = p.find(lookup);
- assert(it != p.end());
- if (it->second.decrement(w, rng))
- backoff.decrement(w, context, rng);
- }
- double prob(WordID w, const vector<WordID>& context) const {
- const double bo = backoff.prob(w, context);
- for (unsigned i = 0; i < N-1; ++i)
- lookup[i] = context[context.size() - 1 - i];
- typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::const_iterator it = p.find(lookup);
- if (it == p.end()) return bo;
- return it->second.prob(w, bo);
- }
-
- double log_likelihood() const {
- double llh = backoff.log_likelihood();
- typename unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > >::const_iterator it;
- for (it = p.begin(); it != p.end(); ++it)
- llh += it->second.log_crp_prob();
- llh += tr.LogLikelihood();
- return llh;
- }
-
- void resample_hyperparameters(MT19937* rng) {
- tr.ResampleHyperparameters(rng);
- backoff.resample_hyperparameters(rng);
- }
-
- PYPLM<N-1> backoff;
- TiedResampler<CCRP<WordID> > tr;
- double discount_a, discount_b, strength_s, strength_r;
- double d, strength;
- mutable vector<WordID> lookup; // thread-local
- unordered_map<vector<WordID>, CCRP<WordID>, boost::hash<vector<WordID> > > p;
-};
-
-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;
- PYPLM<kORDER> 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>());
- vector<WordID> ctx(kORDER - 1, TD::Convert("<s>"));
- for (int SS=0; SS < samples; ++SS) {
- for (int ci = 0; ci < corpuse.size(); ++ci) {
- ctx.resize(kORDER - 1);
- 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, ctx, &rng);
- lm.increment(w, ctx, &rng);
- ctx.push_back(w);
- }
- if (SS > 0) lm.decrement(kEOS, ctx, &rng);
- lm.increment(kEOS, ctx, &rng);
- }
- if (SS % 10 == 9) {
- cerr << " [LLH=" << lm.log_likelihood() << "]" << endl;
- if (SS % 20 == 19) lm.resample_hyperparameters(&rng);
- } else { cerr << '.' << flush; }
- }
- double llh = 0;
- unsigned cnt = 0;
- unsigned oovs = 0;
- for (int ci = 0; ci < test.size(); ++ci) {
- ctx.resize(kORDER - 1);
- 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, ctx)) / log(2);
- if (i < s.size() && vocabe.count(w) == 0) {
- cerr << "**OOV ";
- ++oovs;
- lp = 0;
- }
- cerr << "p(" << TD::Convert(w) << " |";
- for (int j = ctx.size() + 1 - kORDER; j < ctx.size(); ++j)
- cerr << ' ' << TD::Convert(ctx[j]);
- cerr << ") = " << lp << endl;
- ctx.push_back(w);
- 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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