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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;
+
+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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