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-rw-r--r--phrasinator/gibbs_train_plm.notables.cc335
1 files changed, 335 insertions, 0 deletions
diff --git a/phrasinator/gibbs_train_plm.notables.cc b/phrasinator/gibbs_train_plm.notables.cc
new file mode 100644
index 00000000..4b431b90
--- /dev/null
+++ b/phrasinator/gibbs_train_plm.notables.cc
@@ -0,0 +1,335 @@
+#include <iostream>
+#include <tr1/memory>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "filelib.h"
+#include "dict.h"
+#include "sampler.h"
+#include "ccrp.h"
+#include "ccrp_nt.h"
+
+using namespace std;
+using namespace std::tr1;
+namespace po = boost::program_options;
+
+Dict d; // global dictionary
+
+string Join(char joiner, const vector<int>& phrase) {
+ ostringstream os;
+ for (int i = 0; i < phrase.size(); ++i) {
+ if (i > 0) os << joiner;
+ os << d.Convert(phrase[i]);
+ }
+ return os.str();
+}
+
+template <typename BType>
+void WriteSeg(const vector<int>& line, const vector<BType>& label, const Dict& d) {
+ assert(line.size() == label.size());
+ assert(label.back());
+ int prev = 0;
+ int cur = 0;
+ while (cur < line.size()) {
+ if (label[cur]) {
+ if (prev) cout << ' ';
+ cout << "{{";
+ for (int i = prev; i <= cur; ++i)
+ cout << (i == prev ? "" : " ") << d.Convert(line[i]);
+ cout << "}}:" << label[cur];
+ prev = cur + 1;
+ }
+ ++cur;
+ }
+ cout << endl;
+}
+
+ostream& operator<<(ostream& os, const vector<int>& phrase) {
+ for (int i = 0; i < phrase.size(); ++i)
+ os << (i == 0 ? "" : " ") << d.Convert(phrase[i]);
+ return os;
+}
+
+struct UnigramLM {
+ explicit UnigramLM(const string& fname) {
+ ifstream in(fname.c_str());
+ assert(in);
+ }
+
+ double logprob(int word) const {
+ assert(word < freqs_.size());
+ return freqs_[word];
+ }
+
+ vector<double> freqs_;
+};
+
+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")
+ ("input,i",po::value<string>(),"Read file from")
+ ("random_seed,S",po::value<uint32_t>(), "Random seed")
+ ("write_cdec_grammar,g", po::value<string>(), "Write cdec grammar to this file")
+ ("write_cdec_weights,w", po::value<string>(), "Write cdec weights to this file")
+ ("poisson_length,p", "Use a Poisson distribution as the length of a phrase in the base distribuion")
+ ("no_hyperparameter_inference,N", "Disable hyperparameter inference");
+ 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);
+ }
+}
+
+void ReadCorpus(const string& filename, vector<vector<int> >* c, set<int>* vocab) {
+ c->clear();
+ istream* in;
+ if (filename == "-")
+ in = &cin;
+ else
+ in = new ifstream(filename.c_str());
+ assert(*in);
+ string line;
+ while(*in) {
+ getline(*in, line);
+ if (line.empty() && !*in) break;
+ c->push_back(vector<int>());
+ vector<int>& v = c->back();
+ d.ConvertWhitespaceDelimitedLine(line, &v);
+ for (int i = 0; i < v.size(); ++i) vocab->insert(v[i]);
+ }
+ if (in != &cin) delete in;
+}
+
+double log_poisson(unsigned x, const double& lambda) {
+ assert(lambda > 0.0);
+ return log(lambda) * x - lgamma(x + 1) - lambda;
+}
+
+struct UniphraseLM {
+ UniphraseLM(const vector<vector<int> >& corpus,
+ const set<int>& vocab,
+ const po::variables_map& conf) :
+ phrases_(1,1),
+ gen_(1,1),
+ corpus_(corpus),
+ uniform_word_(1.0 / vocab.size()),
+ gen_p0_(0.5),
+ p_end_(0.5),
+ use_poisson_(conf.count("poisson_length") > 0) {}
+
+ double p0(const vector<int>& phrase) const {
+ static vector<double> p0s(10000, 0.0);
+ assert(phrase.size() < 10000);
+ double& p = p0s[phrase.size()];
+ if (p) return p;
+ p = exp(log_p0(phrase));
+ if (!p) {
+ cerr << "0 prob phrase: " << phrase << "\nAssigning std::numeric_limits<double>::min()\n";
+ p = std::numeric_limits<double>::min();
+ }
+ return p;
+ }
+
+ double log_p0(const vector<int>& phrase) const {
+ double len_logprob;
+ if (use_poisson_)
+ len_logprob = log_poisson(phrase.size(), 1.0);
+ else
+ len_logprob = log(1 - p_end_) * (phrase.size() -1) + log(p_end_);
+ return log(uniform_word_) * phrase.size() + len_logprob;
+ }
+
+ double llh() const {
+ double llh = gen_.log_crp_prob();
+ llh += log(gen_p0_) + log(1 - gen_p0_);
+ double llhr = phrases_.log_crp_prob();
+ for (CCRP_NoTable<vector<int> >::const_iterator it = phrases_.begin(); it != phrases_.end(); ++it) {
+ llhr += log_p0(it->first);
+ //llhr += log_p0(it->first);
+ if (!isfinite(llh)) {
+ cerr << it->first << endl;
+ cerr << log_p0(it->first) << endl;
+ abort();
+ }
+ }
+ return llh + llhr;
+ }
+
+ void Sample(unsigned int samples, bool hyp_inf, MT19937* rng) {
+ cerr << "Initializing...\n";
+ z_.resize(corpus_.size());
+ int tc = 0;
+ for (int i = 0; i < corpus_.size(); ++i) {
+ const vector<int>& line = corpus_[i];
+ const int ls = line.size();
+ const int last_pos = ls - 1;
+ vector<bool>& z = z_[i];
+ z.resize(ls);
+ int prev = 0;
+ for (int j = 0; j < ls; ++j) {
+ z[j] = rng->next() < 0.5;
+ if (j == last_pos) z[j] = true; // break phrase at the end of the sentence
+ if (z[j]) {
+ const vector<int> p(line.begin() + prev, line.begin() + j + 1);
+ phrases_.increment(p);
+ //cerr << p << ": " << p0(p) << endl;
+ prev = j + 1;
+ gen_.increment(false);
+ ++tc; // remove
+ }
+ }
+ ++tc;
+ gen_.increment(true); // end of utterance
+ }
+ cerr << "TC: " << tc << endl;
+ cerr << "Initial LLH: " << llh() << endl;
+ cerr << "Sampling...\n";
+ cerr << gen_ << endl;
+ for (int s = 1; s < samples; ++s) {
+ cerr << '.';
+ if (s % 10 == 0) {
+ cerr << " [" << s;
+ if (hyp_inf) ResampleHyperparameters(rng);
+ cerr << " LLH=" << llh() << "]\n";
+ vector<int> z(z_[0].size(), 0);
+ //for (int j = 0; j < z.size(); ++j) z[j] = z_[0][j];
+ //SegCorpus::Write(corpus_[0], z, d);
+ }
+ for (int i = 0; i < corpus_.size(); ++i) {
+ const vector<int>& line = corpus_[i];
+ const int ls = line.size();
+ const int last_pos = ls - 1;
+ vector<bool>& z = z_[i];
+ int prev = 0;
+ for (int j = 0; j < last_pos; ++j) { // don't resample last position
+ int next = j+1; while(!z[next]) { ++next; }
+ const vector<int> p1p2(line.begin() + prev, line.begin() + next + 1);
+ const vector<int> p1(line.begin() + prev, line.begin() + j + 1);
+ const vector<int> p2(line.begin() + j + 1, line.begin() + next + 1);
+
+ if (z[j]) {
+ phrases_.decrement(p1);
+ phrases_.decrement(p2);
+ gen_.decrement(false);
+ gen_.decrement(false);
+ } else {
+ phrases_.decrement(p1p2);
+ gen_.decrement(false);
+ }
+
+ const double d1 = phrases_.prob(p1p2, p0(p1p2)) * gen_.prob(false, gen_p0_);
+ double d2 = phrases_.prob(p1, p0(p1)) * gen_.prob(false, gen_p0_);
+ phrases_.increment(p1);
+ gen_.increment(false);
+ d2 *= phrases_.prob(p2, p0(p2)) * gen_.prob(false, gen_p0_);
+ phrases_.decrement(p1);
+ gen_.decrement(false);
+ z[j] = rng->SelectSample(d1, d2);
+
+ if (z[j]) {
+ phrases_.increment(p1);
+ phrases_.increment(p2);
+ gen_.increment(false);
+ gen_.increment(false);
+ prev = j + 1;
+ } else {
+ phrases_.increment(p1p2);
+ gen_.increment(false);
+ }
+ }
+ }
+ }
+// cerr << endl << endl << gen_ << endl << phrases_ << endl;
+ cerr << gen_.prob(false, gen_p0_) << " " << gen_.prob(true, 1 - gen_p0_) << endl;
+ }
+
+ void WriteCdecGrammarForCurrentSample(ostream* os) const {
+ CCRP_NoTable<vector<int> >::const_iterator it = phrases_.begin();
+ for (; it != phrases_.end(); ++it) {
+ (*os) << "[X] ||| " << Join(' ', it->first) << " ||| "
+ << Join('_', it->first) << " ||| C=1 P="
+ << log(phrases_.prob(it->first, p0(it->first))) << endl;
+ }
+ }
+
+ double OOVUnigramLogProb() const {
+ vector<int> x(1,99999999);
+ return log(phrases_.prob(x, p0(x)));
+ }
+
+ void ResampleHyperparameters(MT19937* rng) {
+ phrases_.resample_hyperparameters(rng);
+ gen_.resample_hyperparameters(rng);
+ cerr << " " << phrases_.concentration();
+ }
+
+ CCRP_NoTable<vector<int> > phrases_;
+ CCRP_NoTable<bool> gen_;
+ vector<vector<bool> > z_; // z_[i] is there a phrase boundary after the ith word
+ const vector<vector<int> >& corpus_;
+ const double uniform_word_;
+ const double gen_p0_;
+ const double p_end_; // in base length distribution, p of the end of a phrase
+ const bool use_poisson_;
+};
+
+
+int main(int argc, char** argv) {
+ po::variables_map conf;
+ InitCommandLine(argc, argv, &conf);
+ shared_ptr<MT19937> prng;
+ 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<int> > corpus;
+ set<int> vocab;
+ ReadCorpus(conf["input"].as<string>(), &corpus, &vocab);
+ cerr << "Corpus size: " << corpus.size() << " sentences\n";
+ cerr << "Vocabulary size: " << vocab.size() << " types\n";
+
+ UniphraseLM ulm(corpus, vocab, conf);
+ ulm.Sample(conf["samples"].as<unsigned>(), conf.count("no_hyperparameter_inference") == 0, &rng);
+ cerr << "OOV unigram prob: " << ulm.OOVUnigramLogProb() << endl;
+
+ for (int i = 0; i < corpus.size(); ++i)
+ WriteSeg(corpus[i], ulm.z_[i], d);
+
+ if (conf.count("write_cdec_grammar")) {
+ string fname = conf["write_cdec_grammar"].as<string>();
+ cerr << "Writing model to " << fname << " ...\n";
+ WriteFile wf(fname);
+ ulm.WriteCdecGrammarForCurrentSample(wf.stream());
+ }
+
+ if (conf.count("write_cdec_weights")) {
+ string fname = conf["write_cdec_weights"].as<string>();
+ cerr << "Writing weights to " << fname << " .\n";
+ WriteFile wf(fname);
+ ostream& os = *wf.stream();
+ os << "# make C smaller to use more phrases\nP 1\nPassThrough " << ulm.OOVUnigramLogProb() << "\nC -3\n";
+ }
+
+
+
+ return 0;
+}
+
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