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, 532 insertions, 0 deletions
diff --git a/dtrain/test/mtm11/mira_update/dtrain.cc b/dtrain/test/mtm11/mira_update/dtrain.cc new file mode 100644 index 00000000..933417a4 --- /dev/null +++ b/dtrain/test/mtm11/mira_update/dtrain.cc @@ -0,0 +1,532 @@ +#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; +} + |