From 29637c966fce216182c0e3d8f17d0ab281edfb67 Mon Sep 17 00:00:00 2001 From: Patrick Simianer Date: Tue, 21 Feb 2012 09:34:59 +0100 Subject: fixed output, removed obsolete files --- dtrain/test/mira_update/Hildreth.cpp | 187 ++++++++++++ dtrain/test/mira_update/Hildreth.h | 10 + dtrain/test/mira_update/dtrain.cc | 532 +++++++++++++++++++++++++++++++++++ dtrain/test/mira_update/sample.h | 101 +++++++ 4 files changed, 830 insertions(+) create mode 100644 dtrain/test/mira_update/Hildreth.cpp create mode 100644 dtrain/test/mira_update/Hildreth.h create mode 100644 dtrain/test/mira_update/dtrain.cc create mode 100644 dtrain/test/mira_update/sample.h (limited to 'dtrain/test/mira_update') diff --git a/dtrain/test/mira_update/Hildreth.cpp b/dtrain/test/mira_update/Hildreth.cpp new file mode 100644 index 00000000..0e67eb15 --- /dev/null +++ b/dtrain/test/mira_update/Hildreth.cpp @@ -0,0 +1,187 @@ +#include "Hildreth.h" +#include "sparse_vector.h" + +using namespace std; + +namespace Mira { + vector Hildreth::optimise (vector< SparseVector >& a, vector& b) { + + size_t i; + int max_iter = 10000; + double eps = 0.00000001; + double zero = 0.000000000001; + + vector alpha ( b.size() ); + vector F ( b.size() ); + vector kkt ( b.size() ); + + double max_kkt = -1e100; + + size_t K = b.size(); + + double A[K][K]; + bool is_computed[K]; + for ( i = 0; i < K; i++ ) + { + A[i][i] = a[i].dot(a[i]); + is_computed[i] = false; + } + + int max_kkt_i = -1; + + + for ( i = 0; i < b.size(); i++ ) + { + F[i] = b[i]; + kkt[i] = F[i]; + if ( kkt[i] > max_kkt ) + { + max_kkt = kkt[i]; + max_kkt_i = i; + } + } + + int iter = 0; + double diff_alpha; + double try_alpha; + double add_alpha; + + while ( max_kkt >= eps && iter < max_iter ) + { + + diff_alpha = A[max_kkt_i][max_kkt_i] <= zero ? 0.0 : F[max_kkt_i]/A[max_kkt_i][max_kkt_i]; + try_alpha = alpha[max_kkt_i] + diff_alpha; + add_alpha = 0.0; + + if ( try_alpha < 0.0 ) + add_alpha = -1.0 * alpha[max_kkt_i]; + else + add_alpha = diff_alpha; + + alpha[max_kkt_i] = alpha[max_kkt_i] + add_alpha; + + if ( !is_computed[max_kkt_i] ) + { + for ( i = 0; i < K; i++ ) + { + A[i][max_kkt_i] = a[i].dot(a[max_kkt_i] ); // for version 1 + //A[i][max_kkt_i] = 0; // for version 1 + is_computed[max_kkt_i] = true; + } + } + + for ( i = 0; i < F.size(); i++ ) + { + F[i] -= add_alpha * A[i][max_kkt_i]; + kkt[i] = F[i]; + if ( alpha[i] > zero ) + kkt[i] = abs ( F[i] ); + } + max_kkt = -1e100; + max_kkt_i = -1; + for ( i = 0; i < F.size(); i++ ) + if ( kkt[i] > max_kkt ) + { + max_kkt = kkt[i]; + max_kkt_i = i; + } + + iter++; + } + + return alpha; + } + + vector Hildreth::optimise (vector< SparseVector >& a, vector& b, double C) { + + size_t i; + int max_iter = 10000; + double eps = 0.00000001; + double zero = 0.000000000001; + + vector alpha ( b.size() ); + vector F ( b.size() ); + vector kkt ( b.size() ); + + double max_kkt = -1e100; + + size_t K = b.size(); + + double A[K][K]; + bool is_computed[K]; + for ( i = 0; i < K; i++ ) + { + A[i][i] = a[i].dot(a[i]); + is_computed[i] = false; + } + + int max_kkt_i = -1; + + + for ( i = 0; i < b.size(); i++ ) + { + F[i] = b[i]; + kkt[i] = F[i]; + if ( kkt[i] > max_kkt ) + { + max_kkt = kkt[i]; + max_kkt_i = i; + } + } + + int iter = 0; + double diff_alpha; + double try_alpha; + double add_alpha; + + while ( max_kkt >= eps && iter < max_iter ) + { + + diff_alpha = A[max_kkt_i][max_kkt_i] <= zero ? 0.0 : F[max_kkt_i]/A[max_kkt_i][max_kkt_i]; + try_alpha = alpha[max_kkt_i] + diff_alpha; + add_alpha = 0.0; + + if ( try_alpha < 0.0 ) + add_alpha = -1.0 * alpha[max_kkt_i]; + else if (try_alpha > C) + add_alpha = C - alpha[max_kkt_i]; + else + add_alpha = diff_alpha; + + alpha[max_kkt_i] = alpha[max_kkt_i] + add_alpha; + + if ( !is_computed[max_kkt_i] ) + { + for ( i = 0; i < K; i++ ) + { + A[i][max_kkt_i] = a[i].dot(a[max_kkt_i] ); // for version 1 + //A[i][max_kkt_i] = 0; // for version 1 + is_computed[max_kkt_i] = true; + } + } + + for ( i = 0; i < F.size(); i++ ) + { + F[i] -= add_alpha * A[i][max_kkt_i]; + kkt[i] = F[i]; + if (alpha[i] > C - zero) + kkt[i]=-kkt[i]; + else if (alpha[i] > zero) + kkt[i] = abs(F[i]); + + } + max_kkt = -1e100; + max_kkt_i = -1; + for ( i = 0; i < F.size(); i++ ) + if ( kkt[i] > max_kkt ) + { + max_kkt = kkt[i]; + max_kkt_i = i; + } + + iter++; + } + + return alpha; + } +} diff --git a/dtrain/test/mira_update/Hildreth.h b/dtrain/test/mira_update/Hildreth.h new file mode 100644 index 00000000..8d791085 --- /dev/null +++ b/dtrain/test/mira_update/Hildreth.h @@ -0,0 +1,10 @@ +#include "sparse_vector.h" + +namespace Mira { + class Hildreth { + public : + static std::vector optimise(std::vector< SparseVector >& a, std::vector& b); + static std::vector optimise(std::vector< SparseVector >& a, std::vector& b, double C); + }; +} + diff --git a/dtrain/test/mira_update/dtrain.cc b/dtrain/test/mira_update/dtrain.cc new file mode 100644 index 00000000..933417a4 --- /dev/null +++ b/dtrain/test/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 +#include +#include +//#include +//#include +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(), "configuration file for cdec" ) + ( "kbest", po::value(&k)->default_value(DTRAIN_DEFAULT_K), "k for kbest" ) + ( "ngrams", po::value(&N)->default_value(DTRAIN_DEFAULT_N), "N for Ngrams" ) + ( "filter", po::value(&f)->default_value("unique"), "filter kbest list" ) + ( "epochs", po::value(&T)->default_value(DTRAIN_DEFAULT_T), "# of iterations T" ) + ( "input", po::value(), "input file" ) + ( "scorer", po::value(&s)->default_value(DTRAIN_DEFAULT_SCORER), "scoring metric" ) + ( "output", po::value(), "output weights file" ) + ( "stop_after", po::value(&stop)->default_value(0), "stop after X input sentences" ) + ( "weights_file", po::value(), "input weights file (e.g. from previous iteration)" ) + ( "wprint", po::value(), "weights to print on each iteration" ) + ( "noup", po::value()->zero_tokens(), "do not update weights" ); + + po::options_description clo("Command Line Options"); + clo.add_options() + ( "config,c", po::value(), "dtrain config file" ) + ( "quiet,q", po::value()->zero_tokens(), "be quiet" ) + ( "update-type", po::value(&update_type)->default_value("mira"), "perceptron or mira" ) + ( "n-pairs", po::value(&n_pairs)->default_value(10), "number of pairs used to compute update" ) + ( "verbose,v", po::value()->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().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() != "unique" + && (*cfg)["filter"].as() != "no" ) { + cerr << "Wrong 'filter' type: '" << (*cfg)["filter"].as() << "'." << 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(); + const size_t N = cfg["ngrams"].as(); + const size_t T = cfg["epochs"].as(); + const size_t stop_after = cfg["stop_after"].as(); + const string filter_type = cfg["filter"].as(); + const string update_type = cfg["update-type"].as(); + const size_t n_pairs = cfg["n-pairs"].as(); + const string output_file = cfg["output"].as(); + 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() << endl; + cout << setw(25) << "input " << "'" << cfg["input"].as() << "'" << endl; + cout << setw(25) << "filter " << "'" << filter_type << "'" << endl; + } + + vector wprint; + if ( cfg.count("wprint") ) { + boost::split( wprint, cfg["wprint"].as(), boost::is_any_of(" ") ); + } + + // setup decoder, observer + register_feature_functions(); + SetSilent(true); + ReadFile ini_rf( cfg["decoder_config"].as() ); + if ( !quiet ) + cout << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as() << "'" << 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(); + double (*scorer)( NgramCounts&, const size_t, const size_t, size_t, vector ); + 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 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() ); + SparseVector lambdas; + weights.InitSparseVector( &lambdas ); + vector dense_weights; + + // input + if ( !quiet && !verbose ) + cout << "(a dot represents " << DTRAIN_DOTS << " lines of input)" << endl; + string input_fn = cfg["input"].as(); + ifstream input; + if ( input_fn != "-" ) input.open( input_fn.c_str() ); + string in; + vector in_split; // input: src\tref\tpsg + vector ref_tok; // tokenized reference + vector ref_ids; // reference as vector of WordID + string grammar_str; + + // buffer input for t > 0 + vector src_str_buf; // source strings, TODO? memory + vector > 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 > 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 > featureValueDiffs; + vector lossMinusModelScoreDiffs; + for ( TrainingInstances::iterator ti = pairs.begin(); + ti != pairs.end(); ti++ ) { + + SparseVector 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 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 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::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 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() << "' ..."; + weights.WriteToFile( cfg["output"].as(), 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; +} + diff --git a/dtrain/test/mira_update/sample.h b/dtrain/test/mira_update/sample.h new file mode 100644 index 00000000..5c331bba --- /dev/null +++ b/dtrain/test/mira_update/sample.h @@ -0,0 +1,101 @@ +#ifndef _DTRAIN_SAMPLE_H_ +#define _DTRAIN_SAMPLE_H_ + + +#include "kbestget.h" + + +namespace dtrain +{ + + +struct TPair +{ + SparseVector first, second; + size_t first_rank, second_rank; + double first_score, second_score; + double model_score_diff; + double loss_diff; +}; + +typedef vector TrainingInstances; + + +void + sample_all( KBestList* kb, TrainingInstances &training, size_t n_pairs ) +{ + std::vector loss_diffs; + TrainingInstances training_tmp; + for ( size_t i = 0; i < kb->GetSize()-1; i++ ) { + for ( size_t j = i+1; j < kb->GetSize(); j++ ) { + TPair p; + p.first = kb->feats[i]; + p.second = kb->feats[j]; + p.first_rank = i; + p.second_rank = j; + p.first_score = kb->scores[i]; + p.second_score = kb->scores[j]; + + bool conservative = 1; + if ( kb->scores[i] - kb->scores[j] < 0 ) { + // j=hope, i=fear + p.model_score_diff = kb->model_scores[j] - kb->model_scores[i]; + p.loss_diff = kb->scores[j] - kb->scores[i]; + training_tmp.push_back(p); + loss_diffs.push_back(p.loss_diff); + } + else if (!conservative) { + // i=hope, j=fear + p.model_score_diff = kb->model_scores[i] - kb->model_scores[j]; + p.loss_diff = kb->scores[i] - kb->scores[j]; + training_tmp.push_back(p); + loss_diffs.push_back(p.loss_diff); + } + } + } + + if (training_tmp.size() > 0) { + double threshold; + std::sort(loss_diffs.begin(), loss_diffs.end()); + std::reverse(loss_diffs.begin(), loss_diffs.end()); + threshold = loss_diffs.size() >= n_pairs ? loss_diffs[n_pairs-1] : loss_diffs[loss_diffs.size()-1]; + cerr << "threshold: " << threshold << endl; + size_t constraints = 0; + for (size_t i = 0; (i < training_tmp.size() && constraints < n_pairs); ++i) { + if (training_tmp[i].loss_diff >= threshold) { + training.push_back(training_tmp[i]); + constraints++; + } + } + } + else { + cerr << "No pairs selected." << endl; + } +} + +void +sample_rand( KBestList* kb, TrainingInstances &training ) +{ + srand( time(NULL) ); + for ( size_t i = 0; i < kb->GetSize()-1; i++ ) { + for ( size_t j = i+1; j < kb->GetSize(); j++ ) { + if ( rand() % 2 ) { + TPair p; + p.first = kb->feats[i]; + p.second = kb->feats[j]; + p.first_rank = i; + p.second_rank = j; + p.first_score = kb->scores[i]; + p.second_score = kb->scores[j]; + training.push_back( p ); + } + } + } +} + + +} // namespace + + +#endif + -- cgit v1.2.3