// STL #include #include #include #include // Boost #include #include #include #include #include #include // 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' <(), "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(), "file containing the documents and context terms") ("topics,t", value()->default_value(50), "number of topics") ("document-topics-out,o", value(), "file to write the document topics to") ("default-topics-out", value(), "file to write default term topic assignments.") ("topic-words-out,w", value(), "file to write the topic word distribution to") ("samples,s", value()->default_value(10), "number of sampling passes through the data") ("backoff-type", value(), "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.") ("binary-counts,b", "Use binary rather than integer counts for contexts.") ("freq-cutoff-start", value()->default_value(0), "initial frequency cutoff.") ("freq-cutoff-end", value()->default_value(0), "final frequency cutoff.") ("freq-cutoff-interval", value()->default_value(0), "number of iterations between frequency decrement.") ("max-contexts-per-document", value()->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().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(), vm.count("hierarchical-topics"), seed); // read the data BackoffGenerator* backoff_gen=0; if (vm.count("backoff-type")) { if (vm["backoff-type"].as() == "none") { backoff_gen = 0; } else if (vm["backoff-type"].as() == "simple") { backoff_gen = new SimpleBackoffGenerator(); } else { cerr << "Backoff type (--backoff-type) must be one of none|simple." <(), backoff_gen, /*vm.count("filter-singleton-contexts")*/ false, vm.count("binary-counts")); 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(), vm["freq-cutoff-start"].as(), vm["freq-cutoff-end"].as(), vm["freq-cutoff-interval"].as(), vm["max-contexts-per-document"].as()); if (vm.count("document-topics-out")) { std::ofstream documents_out((vm["document-topics-out"].as() + ".pyp-process-" + boost::lexical_cast(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 all_terms; for (int document_id=mpi_start; document_id 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::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::const_iterator termIt=unique_terms.begin(); termIt != unique_terms.end(); ++termIt) { if (termIt != unique_terms.begin()) documents_out << " ||| "; vector strings = contexts_corpus.context2string(*termIt); copy(strings.begin(), strings.end(),ostream_iterator(documents_out, " ")); std::pair maxinfo = model.max(document_id, *termIt); documents_out << "||| C=" << maxinfo.first << " P=" << maxinfo.second; } documents_out <().c_str()); for (int p=0; p < world.size(); ++p) { std::string rank_p_prefix((vm["document-topics-out"].as() + ".pyp-process-" + boost::lexical_cast(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().c_str()); default_topics << model.max_topic() <::const_iterator termIt=all_terms.begin(); termIt != all_terms.end(); ++termIt) { vector strings = contexts_corpus.context2string(termIt->first); default_topics << model.max(-1, termIt->first).first << " ||| " << termIt->second << " ||| "; copy(strings.begin(), strings.end(),ostream_iterator(default_topics, " ")); default_topics <().c_str()); model.print_topic_terms(topics_out); topics_out.close(); } cout <