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// STL
#include <iostream>
#include <fstream>
// Boost
#include <boost/program_options/parsers.hpp>
#include <boost/program_options/variables_map.hpp>
#include <boost/scoped_ptr.hpp>
// Local
#include "pyp-topics.hh"
#include "corpus.hh"
#include "contexts_corpus.hh"
#include "gzstream.hh"
#include "mt19937ar.h"
static const char *REVISION = "$Revision: 0.1 $";
// Namespaces
using namespace boost;
using namespace boost::program_options;
using namespace std;
int main(int argc, char **argv)
{
std::cout << "Pitman Yor topic models: Copyright 2010 Phil Blunsom\n";
std::cout << REVISION << '\n' << std::endl;
////////////////////////////////////////////////////////////////////////////////////////////
// Command line processing
variables_map vm;
// Command line processing
options_description cmdline_specific("Command line specific options");
cmdline_specific.add_options()
("help,h", "print help message")
("config,c", value<string>(), "config file specifying additional command line options")
;
options_description generic("Allowed options");
generic.add_options()
("documents,d", value<string>(), "file containing the documents")
("contexts", value<string>(), "file containing the documents in phrase contexts format")
("topics,t", value<int>()->default_value(50), "number of topics")
("document-topics-out,o", value<string>(), "file to write the document topics to")
("topic-words-out,w", value<string>(), "file to write the topic word distribution to")
("samples,s", value<int>()->default_value(10), "number of sampling passes through the data")
("test-corpus", value<string>(), "file containing the test data")
("backoff-paths", value<string>(), "file containing the term backoff paths")
("backoff-type", value<string>(), "backoff type: none|simple")
;
options_description config_options, cmdline_options;
config_options.add(generic);
cmdline_options.add(generic).add(cmdline_specific);
store(parse_command_line(argc, argv, cmdline_options), vm);
if (vm.count("config") > 0) {
ifstream config(vm["config"].as<string>().c_str());
store(parse_config_file(config, cmdline_options), vm);
}
notify(vm);
////////////////////////////////////////////////////////////////////////////////////////////
if (vm.count("contexts") > 0 && vm.count("documents") > 0) {
cerr << "Only one of --documents or --contexts must be specified." << std::endl;
return 1;
}
if (vm.count("documents") == 0 && vm.count("contexts") == 0) {
cerr << "Please specify a file containing the documents." << endl;
cout << cmdline_options << "\n";
return 1;
}
if (vm.count("help")) {
cout << cmdline_options << "\n";
return 1;
}
// seed the random number generator
//mt_init_genrand(time(0));
PYPTopics model(vm["topics"].as<int>());
// read the data
boost::shared_ptr<Corpus> corpus;
if (vm.count("documents") == 0 && vm.count("contexts") == 0) {
corpus.reset(new Corpus);
corpus->read(vm["documents"].as<string>());
// read the backoff dictionary
if (vm.count("backoff-paths"))
model.set_backoff(vm["backoff-paths"].as<string>());
}
else {
BackoffGenerator* backoff_gen=0;
if (vm.count("backoff-type")) {
if (vm["backoff-type"].as<std::string>() == "none") {
backoff_gen = 0;
}
else if (vm["backoff-type"].as<std::string>() == "simple") {
backoff_gen = new SimpleBackoffGenerator();
}
else {
std::cerr << "Backoff type (--backoff-type) must be one of none|simple." << std::endl;
return(1);
}
}
boost::shared_ptr<ContextsCorpus> contexts_corpus(new ContextsCorpus);
contexts_corpus->read_contexts(vm["contexts"].as<string>(), backoff_gen);
corpus = contexts_corpus;
model.set_backoff(contexts_corpus->backoff_index());
if (backoff_gen)
delete backoff_gen;
}
// train the sampler
model.sample(*corpus, vm["samples"].as<int>());
if (vm.count("document-topics-out")) {
ogzstream documents_out(vm["document-topics-out"].as<string>().c_str());
//model.print_document_topics(documents_out);
int document_id=0;
for (Corpus::const_iterator corpusIt=corpus->begin();
corpusIt != corpus->end(); ++corpusIt, ++document_id) {
std::vector<int> unique_terms;
for (Document::const_iterator docIt=corpusIt->begin();
docIt != corpusIt->end(); ++docIt) {
if (unique_terms.empty() || *docIt != unique_terms.back())
unique_terms.push_back(*docIt);
}
documents_out << unique_terms.size();
for (std::vector<int>::const_iterator termIt=unique_terms.begin();
termIt != unique_terms.end(); ++termIt)
documents_out << " " << *termIt << ":" << model.max(document_id, *termIt);
documents_out << std::endl;
}
documents_out.close();
}
if (vm.count("topic-words-out")) {
ogzstream topics_out(vm["topic-words-out"].as<string>().c_str());
model.print_topic_terms(topics_out);
topics_out.close();
}
if (vm.count("test-corpus")) {
TestCorpus test_corpus;
test_corpus.read(vm["test-corpus"].as<string>());
ogzstream topics_out((vm["test-corpus"].as<string>() + ".topics.gz").c_str());
for (TestCorpus::const_iterator corpusIt=test_corpus.begin();
corpusIt != test_corpus.end(); ++corpusIt) {
int index=0;
for (DocumentTerms::const_iterator instanceIt=corpusIt->begin();
instanceIt != corpusIt->end(); ++instanceIt, ++index) {
int topic = model.max(instanceIt->doc, instanceIt->term);
if (index != 0) topics_out << " ";
topics_out << topic;
}
topics_out << std::endl;
}
topics_out.close();
}
std::cout << std::endl;
return 0;
}
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