diff options
Diffstat (limited to 'dtrain/test/mtm11/mira_update/dtrain.cc')
| -rw-r--r-- | dtrain/test/mtm11/mira_update/dtrain.cc | 532 |
1 files changed, 0 insertions, 532 deletions
diff --git a/dtrain/test/mtm11/mira_update/dtrain.cc b/dtrain/test/mtm11/mira_update/dtrain.cc deleted file mode 100644 index 933417a4..00000000 --- a/dtrain/test/mtm11/mira_update/dtrain.cc +++ /dev/null @@ -1,532 +0,0 @@ -#include "common.h" -#include "kbestget.h" -#include "util.h" -#include "sample.h" -#include "Hildreth.h" - -#include "ksampler.h" - -// boost compression -#include <boost/iostreams/device/file.hpp> -#include <boost/iostreams/filtering_stream.hpp> -#include <boost/iostreams/filter/gzip.hpp> -//#include <boost/iostreams/filter/zlib.hpp> -//#include <boost/iostreams/filter/bzip2.hpp> -using namespace boost::iostreams; - - -#ifdef DTRAIN_DEBUG -#include "tests.h" -#endif - - -/* - * init - * - */ -bool -init(int argc, char** argv, po::variables_map* cfg) -{ - po::options_description conff( "Configuration File Options" ); - size_t k, N, T, stop, n_pairs; - string s, f, update_type; - conff.add_options() - ( "decoder_config", po::value<string>(), "configuration file for cdec" ) - ( "kbest", po::value<size_t>(&k)->default_value(DTRAIN_DEFAULT_K), "k for kbest" ) - ( "ngrams", po::value<size_t>(&N)->default_value(DTRAIN_DEFAULT_N), "N for Ngrams" ) - ( "filter", po::value<string>(&f)->default_value("unique"), "filter kbest list" ) - ( "epochs", po::value<size_t>(&T)->default_value(DTRAIN_DEFAULT_T), "# of iterations T" ) - ( "input", po::value<string>(), "input file" ) - ( "scorer", po::value<string>(&s)->default_value(DTRAIN_DEFAULT_SCORER), "scoring metric" ) - ( "output", po::value<string>(), "output weights file" ) - ( "stop_after", po::value<size_t>(&stop)->default_value(0), "stop after X input sentences" ) - ( "weights_file", po::value<string>(), "input weights file (e.g. from previous iteration)" ) - ( "wprint", po::value<string>(), "weights to print on each iteration" ) - ( "noup", po::value<bool>()->zero_tokens(), "do not update weights" ); - - po::options_description clo("Command Line Options"); - clo.add_options() - ( "config,c", po::value<string>(), "dtrain config file" ) - ( "quiet,q", po::value<bool>()->zero_tokens(), "be quiet" ) - ( "update-type", po::value<string>(&update_type)->default_value("mira"), "perceptron or mira" ) - ( "n-pairs", po::value<size_t>(&n_pairs)->default_value(10), "number of pairs used to compute update" ) - ( "verbose,v", po::value<bool>()->zero_tokens(), "be verbose" ) -#ifndef DTRAIN_DEBUG - ; -#else - ( "test", "run tests and exit"); -#endif - po::options_description config_options, cmdline_options; - - config_options.add(conff); - cmdline_options.add(clo); - cmdline_options.add(conff); - - po::store( parse_command_line(argc, argv, cmdline_options), *cfg ); - if ( cfg->count("config") ) { - ifstream config( (*cfg)["config"].as<string>().c_str() ); - po::store( po::parse_config_file(config, config_options), *cfg ); - } - po::notify(*cfg); - - if ( !cfg->count("decoder_config") || !cfg->count("input") ) { - cerr << cmdline_options << endl; - return false; - } - if ( cfg->count("noup") && cfg->count("decode") ) { - cerr << "You can't use 'noup' and 'decode' at once." << endl; - return false; - } - if ( cfg->count("filter") && (*cfg)["filter"].as<string>() != "unique" - && (*cfg)["filter"].as<string>() != "no" ) { - cerr << "Wrong 'filter' type: '" << (*cfg)["filter"].as<string>() << "'." << endl; - } - #ifdef DTRAIN_DEBUG - if ( !cfg->count("test") ) { - cerr << cmdline_options << endl; - return false; - } - #endif - return true; -} - - -// output formatting -ostream& _nopos( ostream& out ) { return out << resetiosflags( ios::showpos ); } -ostream& _pos( ostream& out ) { return out << setiosflags( ios::showpos ); } -ostream& _prec2( ostream& out ) { return out << setprecision(2); } -ostream& _prec5( ostream& out ) { return out << setprecision(5); } - - - - -/* - * dtrain - * - */ -int -main( int argc, char** argv ) -{ - cout << setprecision( 5 ); - // handle most parameters - po::variables_map cfg; - if ( ! init(argc, argv, &cfg) ) exit(1); // something is wrong -#ifdef DTRAIN_DEBUG - if ( cfg.count("test") ) run_tests(); // run tests and exit -#endif - bool quiet = false; - if ( cfg.count("quiet") ) quiet = true; - bool verbose = false; - if ( cfg.count("verbose") ) verbose = true; - bool noup = false; - if ( cfg.count("noup") ) noup = true; - const size_t k = cfg["kbest"].as<size_t>(); - const size_t N = cfg["ngrams"].as<size_t>(); - const size_t T = cfg["epochs"].as<size_t>(); - const size_t stop_after = cfg["stop_after"].as<size_t>(); - const string filter_type = cfg["filter"].as<string>(); - const string update_type = cfg["update-type"].as<string>(); - const size_t n_pairs = cfg["n-pairs"].as<size_t>(); - const string output_file = cfg["output"].as<string>(); - if ( !quiet ) { - cout << endl << "dtrain" << endl << "Parameters:" << endl; - cout << setw(25) << "k " << k << endl; - cout << setw(25) << "N " << N << endl; - cout << setw(25) << "T " << T << endl; - if ( cfg.count("stop-after") ) - cout << setw(25) << "stop_after " << stop_after << endl; - if ( cfg.count("weights") ) - cout << setw(25) << "weights " << cfg["weights"].as<string>() << endl; - cout << setw(25) << "input " << "'" << cfg["input"].as<string>() << "'" << endl; - cout << setw(25) << "filter " << "'" << filter_type << "'" << endl; - } - - vector<string> wprint; - if ( cfg.count("wprint") ) { - boost::split( wprint, cfg["wprint"].as<string>(), boost::is_any_of(" ") ); - } - - // setup decoder, observer - register_feature_functions(); - SetSilent(true); - ReadFile ini_rf( cfg["decoder_config"].as<string>() ); - if ( !quiet ) - cout << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl; - Decoder decoder( ini_rf.stream() ); - //KBestGetter observer( k, filter_type ); - MT19937 rng; - KSampler observer( k, &rng ); - - // scoring metric/scorer - string scorer_str = cfg["scorer"].as<string>(); - double (*scorer)( NgramCounts&, const size_t, const size_t, size_t, vector<float> ); - if ( scorer_str == "bleu" ) { - scorer = &bleu; - } else if ( scorer_str == "stupid_bleu" ) { - scorer = &stupid_bleu; - } else if ( scorer_str == "smooth_bleu" ) { - scorer = &smooth_bleu; - } else if ( scorer_str == "approx_bleu" ) { - scorer = &approx_bleu; - } else { - cerr << "Don't know scoring metric: '" << scorer_str << "', exiting." << endl; - exit(1); - } - // for approx_bleu - NgramCounts global_counts( N ); // counts for 1 best translations - size_t global_hyp_len = 0; // sum hypothesis lengths - size_t global_ref_len = 0; // sum reference lengths - // this is all BLEU implmentations - vector<float> bleu_weights; // we leave this empty -> 1/N; TODO? - if ( !quiet ) cout << setw(26) << "scorer '" << scorer_str << "'" << endl << endl; - - // init weights - Weights weights; - if ( cfg.count("weights") ) weights.InitFromFile( cfg["weights"].as<string>() ); - SparseVector<double> lambdas; - weights.InitSparseVector( &lambdas ); - vector<double> dense_weights; - - // input - if ( !quiet && !verbose ) - cout << "(a dot represents " << DTRAIN_DOTS << " lines of input)" << endl; - string input_fn = cfg["input"].as<string>(); - ifstream input; - if ( input_fn != "-" ) input.open( input_fn.c_str() ); - string in; - vector<string> in_split; // input: src\tref\tpsg - vector<string> ref_tok; // tokenized reference - vector<WordID> ref_ids; // reference as vector of WordID - string grammar_str; - - // buffer input for t > 0 - vector<string> src_str_buf; // source strings, TODO? memory - vector<vector<WordID> > ref_ids_buf; // references as WordID vecs - filtering_ostream grammar_buf; // written to compressed file in /tmp - // this is for writing the grammar buffer file - grammar_buf.push( gzip_compressor() ); - char grammar_buf_tmp_fn[] = DTRAIN_TMP_DIR"/dtrain-grammars-XXXXXX"; - mkstemp( grammar_buf_tmp_fn ); - grammar_buf.push( file_sink(grammar_buf_tmp_fn, ios::binary | ios::trunc) ); - - size_t sid = 0, in_sz = 99999999; // sentence id, input size - double acc_1best_score = 0., acc_1best_model = 0.; - vector<vector<double> > scores_per_iter; - double max_score = 0.; - size_t best_t = 0; - bool next = false, stop = false; - double score = 0.; - size_t cand_len = 0; - double overall_time = 0.; - - // for the perceptron/SVM; TODO as params - double eta = 0.0005; - double gamma = 0.;//01; // -> SVM - lambdas.add_value( FD::Convert("__bias"), 0 ); - - // for random sampling - srand ( time(NULL) ); - - - for ( size_t t = 0; t < T; t++ ) // T epochs - { - - time_t start, end; - time( &start ); - - // actually, we need only need this if t > 0 FIXME - ifstream grammar_file( grammar_buf_tmp_fn, ios_base::in | ios_base::binary ); - filtering_istream grammar_buf_in; - grammar_buf_in.push( gzip_decompressor() ); - grammar_buf_in.push( grammar_file ); - - // reset average scores - acc_1best_score = acc_1best_model = 0.; - - // reset sentence counter - sid = 0; - - if ( !quiet ) cout << "Iteration #" << t+1 << " of " << T << "." << endl; - - while( true ) - { - - // get input from stdin or file - in.clear(); - next = stop = false; // next iteration, premature stop - if ( t == 0 ) { - if ( input_fn == "-" ) { - if ( !getline(cin, in) ) next = true; - } else { - if ( !getline(input, in) ) next = true; - } - } else { - if ( sid == in_sz ) next = true; // stop if we reach the end of our input - } - // stop after X sentences (but still iterate for those) - if ( stop_after > 0 && stop_after == sid && !next ) stop = true; - - // produce some pretty output - if ( !quiet && !verbose ) { - if ( sid == 0 ) cout << " "; - if ( (sid+1) % (DTRAIN_DOTS) == 0 ) { - cout << "."; - cout.flush(); - } - if ( (sid+1) % (20*DTRAIN_DOTS) == 0) { - cout << " " << sid+1 << endl; - if ( !next && !stop ) cout << " "; - } - if ( stop ) { - if ( sid % (20*DTRAIN_DOTS) != 0 ) cout << " " << sid << endl; - cout << "Stopping after " << stop_after << " input sentences." << endl; - } else { - if ( next ) { - if ( sid % (20*DTRAIN_DOTS) != 0 ) { - cout << " " << sid << endl; - } - } - } - } - - // next iteration - if ( next || stop ) break; - - // weights - dense_weights.clear(); - weights.InitFromVector( lambdas ); - weights.InitVector( &dense_weights ); - decoder.SetWeights( dense_weights ); - - if ( t == 0 ) { - // handling input - in_split.clear(); - boost::split( in_split, in, boost::is_any_of("\t") ); // in_split[0] is id - // getting reference - ref_tok.clear(); ref_ids.clear(); - boost::split( ref_tok, in_split[2], boost::is_any_of(" ") ); - register_and_convert( ref_tok, ref_ids ); - ref_ids_buf.push_back( ref_ids ); - // process and set grammar - bool broken_grammar = true; - for ( string::iterator ti = in_split[3].begin(); ti != in_split[3].end(); ti++ ) { - if ( !isspace(*ti) ) { - broken_grammar = false; - break; - } - } - if ( broken_grammar ) continue; - grammar_str = boost::replace_all_copy( in_split[3], " __NEXT__RULE__ ", "\n" ) + "\n"; // FIXME copy, __ - grammar_buf << grammar_str << DTRAIN_GRAMMAR_DELIM << endl; - decoder.SetSentenceGrammarFromString( grammar_str ); - // decode, kbest - src_str_buf.push_back( in_split[1] ); - decoder.Decode( in_split[1], &observer ); - } else { - // get buffered grammar - grammar_str.clear(); - int i = 1; - while ( true ) { - string g; - getline( grammar_buf_in, g ); - if ( g == DTRAIN_GRAMMAR_DELIM ) break; - grammar_str += g+"\n"; - i += 1; - } - decoder.SetSentenceGrammarFromString( grammar_str ); - // decode, kbest - decoder.Decode( src_str_buf[sid], &observer ); - } - - // get kbest list - KBestList* kb; - //if ( ) { // TODO get from forest - kb = observer.GetKBest(); - //} - - // scoring kbest - if ( t > 0 ) ref_ids = ref_ids_buf[sid]; - for ( size_t i = 0; i < kb->GetSize(); i++ ) { - NgramCounts counts = make_ngram_counts( ref_ids, kb->sents[i], N ); - // this is for approx bleu - if ( scorer_str == "approx_bleu" ) { - if ( i == 0 ) { // 'context of 1best translations' - global_counts += counts; - global_hyp_len += kb->sents[i].size(); - global_ref_len += ref_ids.size(); - counts.reset(); - cand_len = 0; - } else { - cand_len = kb->sents[i].size(); - } - NgramCounts counts_tmp = global_counts + counts; - // TODO as param - score = 0.9 * scorer( counts_tmp, - global_ref_len, - global_hyp_len + cand_len, N, bleu_weights ); - } else { - // other scorers - cand_len = kb->sents[i].size(); - score = scorer( counts, - ref_ids.size(), - kb->sents[i].size(), N, bleu_weights ); - } - - kb->scores.push_back( score ); - - if ( i == 0 ) { - acc_1best_score += score; - acc_1best_model += kb->model_scores[i]; - } - - if ( verbose ) { - if ( i == 0 ) cout << "'" << TD::GetString( ref_ids ) << "' [ref]" << endl; - cout << _prec5 << _nopos << "[hyp " << i << "] " << "'" << TD::GetString( kb->sents[i] ) << "'"; - cout << " [SCORE=" << score << ",model="<< kb->model_scores[i] << "]" << endl; - cout << kb->feats[i] << endl; // this is maybe too verbose - } - } // Nbest loop - - if ( verbose ) cout << endl; - - - // UPDATE WEIGHTS - if ( !noup ) { - - TrainingInstances pairs; - sample_all( kb, pairs, n_pairs ); - - vector< SparseVector<double> > featureValueDiffs; - vector<double> lossMinusModelScoreDiffs; - for ( TrainingInstances::iterator ti = pairs.begin(); - ti != pairs.end(); ti++ ) { - - SparseVector<double> dv; - if ( ti->first_score - ti->second_score < 0 ) { - dv = ti->second - ti->first; - dv.add_value( FD::Convert("__bias"), -1 ); - - featureValueDiffs.push_back(dv); - double lossMinusModelScoreDiff = ti->loss_diff - ti->model_score_diff; - lossMinusModelScoreDiffs.push_back(lossMinusModelScoreDiff); - - if (update_type == "perceptron") { - lambdas += dv * eta; - cerr << "after perceptron update: " << lambdas << endl << endl; - } - - if ( verbose ) { - cout << "{{ f("<< ti->first_rank <<") > f(" << ti->second_rank << ") but g(i)="<< ti->first_score <<" < g(j)="<< ti->second_score << " so update" << endl; - cout << " i " << TD::GetString(kb->sents[ti->first_rank]) << endl; - cout << " " << kb->feats[ti->first_rank] << endl; - cout << " j " << TD::GetString(kb->sents[ti->second_rank]) << endl; - cout << " " << kb->feats[ti->second_rank] << endl; - cout << " diff vec: " << dv << endl; - cout << " lambdas after update: " << lambdas << endl; - cout << "}}" << endl; - } - } else { - //SparseVector<double> reg; - //reg = lambdas * ( 2 * gamma ); - //lambdas += reg * ( -eta ); - } - } - cerr << "Collected " << featureValueDiffs.size() << " constraints." << endl; - - double slack = 0.01; - if (update_type == "mira") { - if (featureValueDiffs.size() > 0) { - vector<double> alphas; - if (slack != 0) { - alphas = Mira::Hildreth::optimise(featureValueDiffs, lossMinusModelScoreDiffs, slack); - } else { - alphas = Mira::Hildreth::optimise(featureValueDiffs, lossMinusModelScoreDiffs); - } - - for (size_t k = 0; k < featureValueDiffs.size(); ++k) { - lambdas += featureValueDiffs[k] * alphas[k]; - } - // cerr << "after mira update: " << lambdas << endl << endl; - } - } - } - - ++sid; - - } // input loop - - if ( t == 0 ) in_sz = sid; // remember size (lines) of input - - // print some stats - double avg_1best_score = acc_1best_score/(double)in_sz; - double avg_1best_model = acc_1best_model/(double)in_sz; - double avg_1best_score_diff, avg_1best_model_diff; - if ( t > 0 ) { - avg_1best_score_diff = avg_1best_score - scores_per_iter[t-1][0]; - avg_1best_model_diff = avg_1best_model - scores_per_iter[t-1][1]; - } else { - avg_1best_score_diff = avg_1best_score; - avg_1best_model_diff = avg_1best_model; - } - cout << _prec5 << _pos << "WEIGHTS" << endl; - for (vector<string>::iterator it = wprint.begin(); it != wprint.end(); it++) { - cout << setw(16) << *it << " = " << dense_weights[FD::Convert( *it )] << endl; - } - - cout << " ---" << endl; - cout << _nopos << " avg score: " << avg_1best_score; - cout << _pos << " (" << avg_1best_score_diff << ")" << endl; - cout << _nopos << "avg model score: " << avg_1best_model; - cout << _pos << " (" << avg_1best_model_diff << ")" << endl; - vector<double> remember_scores; - remember_scores.push_back( avg_1best_score ); - remember_scores.push_back( avg_1best_model ); - scores_per_iter.push_back( remember_scores ); - if ( avg_1best_score > max_score ) { - max_score = avg_1best_score; - best_t = t; - } - - // close open files - if ( input_fn != "-" ) input.close(); - close( grammar_buf ); - grammar_file.close(); - - time ( &end ); - double time_dif = difftime( end, start ); - overall_time += time_dif; - if ( !quiet ) { - cout << _prec2 << _nopos << "(time " << time_dif/60. << " min, "; - cout << time_dif/(double)in_sz<< " s/S)" << endl; - } - - if ( t+1 != T ) cout << endl; - - if ( noup ) break; - - // write weights after every epoch - std::string s; - std::stringstream out; - out << t; - s = out.str(); - string weights_file = output_file + "." + s; - weights.WriteToFile(weights_file, true ); - - } // outer loop - - unlink( grammar_buf_tmp_fn ); - if ( !noup ) { - if ( !quiet ) cout << endl << "writing weights file '" << cfg["output"].as<string>() << "' ..."; - weights.WriteToFile( cfg["output"].as<string>(), true ); - if ( !quiet ) cout << "done" << endl; - } - - if ( !quiet ) { - cout << _prec5 << _nopos << endl << "---" << endl << "Best iteration: "; - cout << best_t+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl; - cout << _prec2 << "This took " << overall_time/60. << " min." << endl; - } - - return 0; -} - |