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// STL
#include <iostream>
#include <fstream>
#include <algorithm>
#include <iterator>
// Boost
#include <boost/program_options/parsers.hpp>
#include <boost/program_options/variables_map.hpp>
#include <boost/scoped_ptr.hpp>
#include <boost/mpi/environment.hpp>
#include <boost/mpi/communicator.hpp>
#include <boost/lexical_cast.hpp>
// Local
#include "mpi-pyp-topics.hh"
#include "corpus.hh"
#include "mpi-corpus.hh"
#include "gzstream.hh"
static const char *REVISION = "$Rev: 170 $";
// Namespaces
using namespace boost;
using namespace boost::program_options;
using namespace std;
int main(int argc, char **argv)
{
mpi::environment env(argc, argv);
mpi::communicator world;
int rank = world.rank();
bool am_root = (rank==0);
if (am_root) cout << "Pitman Yor topic models: Copyright 2010 Phil Blunsom\n";
if (am_root) std::cout << "I am process " << world.rank() << " of " << world.size() << "." << std::endl;
if (am_root) cout << REVISION << '\n' <<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 config_options("Allowed options");
config_options.add_options()
("help,h", "print help message")
("data,d", value<string>(), "file containing the documents and context terms")
("topics,t", value<int>()->default_value(50), "number of topics")
("document-topics-out,o", value<string>(), "file to write the document topics to")
("default-topics-out", value<string>(), "file to write default term topic assignments.")
("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")
("backoff-type", value<string>(), "backoff type: none|simple")
// ("filter-singleton-contexts", "filter singleton contexts")
("hierarchical-topics", "Use a backoff hierarchical PYP as the P0 for the document topics distribution.")
("freq-cutoff-start", value<int>()->default_value(0), "initial frequency cutoff.")
("freq-cutoff-end", value<int>()->default_value(0), "final frequency cutoff.")
("freq-cutoff-interval", value<int>()->default_value(0), "number of iterations between frequency decrement.")
("max-contexts-per-document", value<int>()->default_value(0), "Only sample the n most frequent contexts for a document.")
;
cmdline_specific.add(config_options);
store(parse_command_line(argc, argv, cmdline_specific), vm);
notify(vm);
if (vm.count("config") > 0) {
ifstream config(vm["config"].as<string>().c_str());
store(parse_config_file(config, config_options), vm);
}
if (vm.count("help")) {
cout << cmdline_specific << "\n";
return 1;
}
}
////////////////////////////////////////////////////////////////////////////////////////////
if (!vm.count("data")) {
cerr << "Please specify a file containing the data." << endl;
return 1;
}
// seed the random number generator: 0 = automatic, specify value otherwise
unsigned long seed = 0;
MPIPYPTopics model(vm["topics"].as<int>(), vm.count("hierarchical-topics"), seed);
// read the data
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 {
cerr << "Backoff type (--backoff-type) must be one of none|simple." <<endl;
return(1);
}
}
//ContextsCorpus contexts_corpus;
MPICorpus contexts_corpus;
contexts_corpus.read_contexts(vm["data"].as<string>(), backoff_gen, /*vm.count("filter-singleton-contexts")*/ false);
int mpi_start = 0, mpi_end = 0;
contexts_corpus.bounds(&mpi_start, &mpi_end);
std::cerr << "\tProcess " << rank << " has documents " << mpi_start << " -> " << mpi_end << "." << std::endl;
model.set_backoff(contexts_corpus.backoff_index());
if (backoff_gen)
delete backoff_gen;
// train the sampler
model.sample_corpus(contexts_corpus, vm["samples"].as<int>(),
vm["freq-cutoff-start"].as<int>(),
vm["freq-cutoff-end"].as<int>(),
vm["freq-cutoff-interval"].as<int>(),
vm["max-contexts-per-document"].as<int>());
if (vm.count("document-topics-out")) {
std::ofstream documents_out((vm["document-topics-out"].as<string>() + ".pyp-process-" + boost::lexical_cast<std::string>(rank)).c_str());
//int documents = contexts_corpus.num_documents();
/*
int mpi_start = 0, mpi_end = documents;
if (world.size() != 1) {
mpi_start = (documents / world.size()) * rank;
if (rank == world.size()-1) mpi_end = documents;
else mpi_end = (documents / world.size())*(rank+1);
}
*/
map<int,int> all_terms;
for (int document_id=mpi_start; document_id<mpi_end; ++document_id) {
assert (document_id < contexts_corpus.num_documents());
const Document& doc = contexts_corpus.at(document_id);
vector<int> unique_terms;
for (Document::const_iterator docIt=doc.begin(); docIt != doc.end(); ++docIt) {
if (unique_terms.empty() || *docIt != unique_terms.back())
unique_terms.push_back(*docIt);
// increment this terms frequency
pair<map<int,int>::iterator,bool> insert_result = all_terms.insert(make_pair(*docIt,1));
if (!insert_result.second)
all_terms[*docIt] = all_terms[*docIt] + 1;
}
documents_out << contexts_corpus.key(document_id) << '\t';
documents_out << model.max(document_id).first << " " << doc.size() << " ||| ";
for (std::vector<int>::const_iterator termIt=unique_terms.begin(); termIt != unique_terms.end(); ++termIt) {
if (termIt != unique_terms.begin())
documents_out << " ||| ";
vector<std::string> strings = contexts_corpus.context2string(*termIt);
copy(strings.begin(), strings.end(),ostream_iterator<std::string>(documents_out, " "));
std::pair<int,MPIPYPTopics::F> maxinfo = model.max(document_id, *termIt);
documents_out << "||| C=" << maxinfo.first << " P=" << maxinfo.second;
}
documents_out <<endl;
}
documents_out.close();
world.barrier();
if (am_root) {
ogzstream root_documents_out(vm["document-topics-out"].as<string>().c_str());
for (int p=0; p < world.size(); ++p) {
std::string rank_p_prefix((vm["document-topics-out"].as<string>() + ".pyp-process-" + boost::lexical_cast<std::string>(p)).c_str());
std::ifstream rank_p_trees_istream(rank_p_prefix.c_str(), std::ios_base::binary);
root_documents_out << rank_p_trees_istream.rdbuf();
rank_p_trees_istream.close();
remove((rank_p_prefix).c_str());
}
root_documents_out.close();
}
if (am_root && vm.count("default-topics-out")) {
ofstream default_topics(vm["default-topics-out"].as<string>().c_str());
default_topics << model.max_topic() <<endl;
for (std::map<int,int>::const_iterator termIt=all_terms.begin(); termIt != all_terms.end(); ++termIt) {
vector<std::string> strings = contexts_corpus.context2string(termIt->first);
default_topics << model.max(-1, termIt->first).first << " ||| " << termIt->second << " ||| ";
copy(strings.begin(), strings.end(),ostream_iterator<std::string>(default_topics, " "));
default_topics <<endl;
}
}
}
if (am_root && 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();
}
cout <<endl;
return 0;
}
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