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-rw-r--r--training/dtrain/dtrain.cc553
1 files changed, 553 insertions, 0 deletions
diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
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+++ b/training/dtrain/dtrain.cc
@@ -0,0 +1,553 @@
+#include "dtrain.h"
+
+
+bool
+dtrain_init(int argc, char** argv, po::variables_map* cfg)
+{
+ po::options_description ini("Configuration File Options");
+ ini.add_options()
+ ("input", po::value<string>()->default_value("-"), "input file (src)")
+ ("refs,r", po::value<string>(), "references")
+ ("output", po::value<string>()->default_value("-"), "output weights file, '-' for STDOUT")
+ ("input_weights", po::value<string>(), "input weights file (e.g. from previous iteration)")
+ ("decoder_config", po::value<string>(), "configuration file for cdec")
+ ("print_weights", po::value<string>(), "weights to print on each iteration")
+ ("stop_after", po::value<unsigned>()->default_value(0), "stop after X input sentences")
+ ("keep", po::value<bool>()->zero_tokens(), "keep weights files for each iteration")
+ ("epochs", po::value<unsigned>()->default_value(10), "# of iterations T (per shard)")
+ ("k", po::value<unsigned>()->default_value(100), "how many translations to sample")
+ ("sample_from", po::value<string>()->default_value("kbest"), "where to sample translations from: 'kbest', 'forest'")
+ ("filter", po::value<string>()->default_value("uniq"), "filter kbest list: 'not', 'uniq'")
+ ("pair_sampling", po::value<string>()->default_value("XYX"), "how to sample pairs: 'all', 'XYX' or 'PRO'")
+ ("hi_lo", po::value<float>()->default_value(0.1), "hi and lo (X) for XYX (default 0.1), <= 0.5")
+ ("pair_threshold", po::value<score_t>()->default_value(0.), "bleu [0,1] threshold to filter pairs")
+ ("N", po::value<unsigned>()->default_value(4), "N for Ngrams (BLEU)")
+ ("scorer", po::value<string>()->default_value("stupid_bleu"), "scoring: bleu, stupid_, smooth_, approx_, lc_")
+ ("learning_rate", po::value<weight_t>()->default_value(1.0), "learning rate")
+ ("gamma", po::value<weight_t>()->default_value(0.), "gamma for SVM (0 for perceptron)")
+ ("select_weights", po::value<string>()->default_value("last"), "output best, last, avg weights ('VOID' to throw away)")
+ ("rescale", po::value<bool>()->zero_tokens(), "rescale weight vector after each input")
+ ("l1_reg", po::value<string>()->default_value("none"), "apply l1 regularization as in 'Tsuroka et al' (2010) UNTESTED")
+ ("l1_reg_strength", po::value<weight_t>(), "l1 regularization strength")
+ ("fselect", po::value<weight_t>()->default_value(-1), "select top x percent (or by threshold) of features after each epoch NOT IMPLEMENTED") // TODO
+ ("approx_bleu_d", po::value<score_t>()->default_value(0.9), "discount for approx. BLEU")
+ ("scale_bleu_diff", po::value<bool>()->zero_tokens(), "learning rate <- bleu diff of a misranked pair")
+ ("loss_margin", po::value<weight_t>()->default_value(0.), "update if no error in pref pair but model scores this near")
+ ("max_pairs", po::value<unsigned>()->default_value(std::numeric_limits<unsigned>::max()), "max. # of pairs per Sent.")
+ ("noup", po::value<bool>()->zero_tokens(), "do not update weights");
+ po::options_description cl("Command Line Options");
+ cl.add_options()
+ ("config,c", po::value<string>(), "dtrain config file")
+ ("quiet,q", po::value<bool>()->zero_tokens(), "be quiet")
+ ("verbose,v", po::value<bool>()->zero_tokens(), "be verbose");
+ cl.add(ini);
+ po::store(parse_command_line(argc, argv, cl), *cfg);
+ if (cfg->count("config")) {
+ ifstream ini_f((*cfg)["config"].as<string>().c_str());
+ po::store(po::parse_config_file(ini_f, ini), *cfg);
+ }
+ po::notify(*cfg);
+ if (!cfg->count("decoder_config")) {
+ cerr << cl << endl;
+ return false;
+ }
+ if ((*cfg)["sample_from"].as<string>() != "kbest"
+ && (*cfg)["sample_from"].as<string>() != "forest") {
+ cerr << "Wrong 'sample_from' param: '" << (*cfg)["sample_from"].as<string>() << "', use 'kbest' or 'forest'." << endl;
+ return false;
+ }
+ if ((*cfg)["sample_from"].as<string>() == "kbest" && (*cfg)["filter"].as<string>() != "uniq" &&
+ (*cfg)["filter"].as<string>() != "not") {
+ cerr << "Wrong 'filter' param: '" << (*cfg)["filter"].as<string>() << "', use 'uniq' or 'not'." << endl;
+ return false;
+ }
+ if ((*cfg)["pair_sampling"].as<string>() != "all" && (*cfg)["pair_sampling"].as<string>() != "XYX" &&
+ (*cfg)["pair_sampling"].as<string>() != "PRO") {
+ cerr << "Wrong 'pair_sampling' param: '" << (*cfg)["pair_sampling"].as<string>() << "'." << endl;
+ return false;
+ }
+ if(cfg->count("hi_lo") && (*cfg)["pair_sampling"].as<string>() != "XYX") {
+ cerr << "Warning: hi_lo only works with pair_sampling XYX." << endl;
+ }
+ if((*cfg)["hi_lo"].as<float>() > 0.5 || (*cfg)["hi_lo"].as<float>() < 0.01) {
+ cerr << "hi_lo must lie in [0.01, 0.5]" << endl;
+ return false;
+ }
+ if ((*cfg)["pair_threshold"].as<score_t>() < 0) {
+ cerr << "The threshold must be >= 0!" << endl;
+ return false;
+ }
+ if ((*cfg)["select_weights"].as<string>() != "last" && (*cfg)["select_weights"].as<string>() != "best" &&
+ (*cfg)["select_weights"].as<string>() != "avg" && (*cfg)["select_weights"].as<string>() != "VOID") {
+ cerr << "Wrong 'select_weights' param: '" << (*cfg)["select_weights"].as<string>() << "', use 'last' or 'best'." << endl;
+ return false;
+ }
+ return true;
+}
+
+int
+main(int argc, char** argv)
+{
+ // handle most parameters
+ po::variables_map cfg;
+ if (!dtrain_init(argc, argv, &cfg)) exit(1); // something is wrong
+ 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;
+ bool rescale = false;
+ if (cfg.count("rescale")) rescale = true;
+ bool keep = false;
+ if (cfg.count("keep")) keep = true;
+
+ const unsigned k = cfg["k"].as<unsigned>();
+ const unsigned N = cfg["N"].as<unsigned>();
+ const unsigned T = cfg["epochs"].as<unsigned>();
+ const unsigned stop_after = cfg["stop_after"].as<unsigned>();
+ const string filter_type = cfg["filter"].as<string>();
+ const string sample_from = cfg["sample_from"].as<string>();
+ const string pair_sampling = cfg["pair_sampling"].as<string>();
+ const score_t pair_threshold = cfg["pair_threshold"].as<score_t>();
+ const string select_weights = cfg["select_weights"].as<string>();
+ const float hi_lo = cfg["hi_lo"].as<float>();
+ const score_t approx_bleu_d = cfg["approx_bleu_d"].as<score_t>();
+ const unsigned max_pairs = cfg["max_pairs"].as<unsigned>();
+ weight_t loss_margin = cfg["loss_margin"].as<weight_t>();
+ if (loss_margin > 9998.) loss_margin = std::numeric_limits<float>::max();
+ bool scale_bleu_diff = false;
+ if (cfg.count("scale_bleu_diff")) scale_bleu_diff = true;
+ bool average = false;
+ if (select_weights == "avg")
+ average = true;
+ vector<string> print_weights;
+ if (cfg.count("print_weights"))
+ boost::split(print_weights, cfg["print_weights"].as<string>(), boost::is_any_of(" "));
+
+
+ // setup decoder
+ register_feature_functions();
+ SetSilent(true);
+ ReadFile ini_rf(cfg["decoder_config"].as<string>());
+ if (!quiet)
+ cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
+ Decoder decoder(ini_rf.stream());
+
+ // scoring metric/scorer
+ string scorer_str = cfg["scorer"].as<string>();
+ LocalScorer* scorer;
+ if (scorer_str == "bleu") {
+ scorer = dynamic_cast<BleuScorer*>(new BleuScorer);
+ } else if (scorer_str == "stupid_bleu") {
+ scorer = dynamic_cast<StupidBleuScorer*>(new StupidBleuScorer);
+ } else if (scorer_str == "fixed_stupid_bleu") {
+ scorer = dynamic_cast<FixedStupidBleuScorer*>(new FixedStupidBleuScorer);
+ } else if (scorer_str == "smooth_bleu") {
+ scorer = dynamic_cast<SmoothBleuScorer*>(new SmoothBleuScorer);
+ } else if (scorer_str == "sum_bleu") {
+ scorer = dynamic_cast<SumBleuScorer*>(new SumBleuScorer);
+ } else if (scorer_str == "sumexp_bleu") {
+ scorer = dynamic_cast<SumExpBleuScorer*>(new SumExpBleuScorer);
+ } else if (scorer_str == "sumwhatever_bleu") {
+ scorer = dynamic_cast<SumWhateverBleuScorer*>(new SumWhateverBleuScorer);
+ } else if (scorer_str == "approx_bleu") {
+ scorer = dynamic_cast<ApproxBleuScorer*>(new ApproxBleuScorer(N, approx_bleu_d));
+ } else if (scorer_str == "lc_bleu") {
+ scorer = dynamic_cast<LinearBleuScorer*>(new LinearBleuScorer(N));
+ } else {
+ cerr << "Don't know scoring metric: '" << scorer_str << "', exiting." << endl;
+ exit(1);
+ }
+ vector<score_t> bleu_weights;
+ scorer->Init(N, bleu_weights);
+
+ // setup decoder observer
+ MT19937 rng; // random number generator, only for forest sampling
+ HypSampler* observer;
+ if (sample_from == "kbest")
+ observer = dynamic_cast<KBestGetter*>(new KBestGetter(k, filter_type));
+ else
+ observer = dynamic_cast<KSampler*>(new KSampler(k, &rng));
+ observer->SetScorer(scorer);
+
+ // init weights
+ vector<weight_t>& dense_weights = decoder.CurrentWeightVector();
+ SparseVector<weight_t> lambdas, cumulative_penalties, w_average;
+ if (cfg.count("input_weights")) Weights::InitFromFile(cfg["input_weights"].as<string>(), &dense_weights);
+ Weights::InitSparseVector(dense_weights, &lambdas);
+
+ // meta params for perceptron, SVM
+ weight_t eta = cfg["learning_rate"].as<weight_t>();
+ weight_t gamma = cfg["gamma"].as<weight_t>();
+
+ // faster perceptron: consider only misranked pairs, see
+ // DO NOT ENABLE WITH SVM (gamma > 0) OR loss_margin!
+ bool faster_perceptron = false;
+ if (gamma==0 && loss_margin==0) faster_perceptron = true;
+
+ // l1 regularization
+ bool l1naive = false;
+ bool l1clip = false;
+ bool l1cumul = false;
+ weight_t l1_reg = 0;
+ if (cfg["l1_reg"].as<string>() != "none") {
+ string s = cfg["l1_reg"].as<string>();
+ if (s == "naive") l1naive = true;
+ else if (s == "clip") l1clip = true;
+ else if (s == "cumul") l1cumul = true;
+ l1_reg = cfg["l1_reg_strength"].as<weight_t>();
+ }
+
+ // output
+ string output_fn = cfg["output"].as<string>();
+ // input
+ string input_fn = cfg["input"].as<string>();
+ ReadFile input(input_fn);
+ // buffer input for t > 0
+ vector<string> src_str_buf; // source strings (decoder takes only strings)
+ vector<vector<WordID> > ref_ids_buf; // references as WordID vecs
+ string refs_fn = cfg["refs"].as<string>();
+ ReadFile refs(refs_fn);
+
+ unsigned in_sz = std::numeric_limits<unsigned>::max(); // input index, input size
+ vector<pair<score_t, score_t> > all_scores;
+ score_t max_score = 0.;
+ unsigned best_it = 0;
+ float overall_time = 0.;
+
+ // output cfg
+ if (!quiet) {
+ cerr << _p5;
+ cerr << endl << "dtrain" << endl << "Parameters:" << endl;
+ cerr << setw(25) << "k " << k << endl;
+ cerr << setw(25) << "N " << N << endl;
+ cerr << setw(25) << "T " << T << endl;
+ cerr << setw(26) << "scorer '" << scorer_str << "'" << endl;
+ if (scorer_str == "approx_bleu")
+ cerr << setw(25) << "approx. B discount " << approx_bleu_d << endl;
+ cerr << setw(25) << "sample from " << "'" << sample_from << "'" << endl;
+ if (sample_from == "kbest")
+ cerr << setw(25) << "filter " << "'" << filter_type << "'" << endl;
+ if (!scale_bleu_diff) cerr << setw(25) << "learning rate " << eta << endl;
+ else cerr << setw(25) << "learning rate " << "bleu diff" << endl;
+ cerr << setw(25) << "gamma " << gamma << endl;
+ cerr << setw(25) << "loss margin " << loss_margin << endl;
+ cerr << setw(25) << "faster perceptron " << faster_perceptron << endl;
+ cerr << setw(25) << "pairs " << "'" << pair_sampling << "'" << endl;
+ if (pair_sampling == "XYX")
+ cerr << setw(25) << "hi lo " << hi_lo << endl;
+ cerr << setw(25) << "pair threshold " << pair_threshold << endl;
+ cerr << setw(25) << "select weights " << "'" << select_weights << "'" << endl;
+ if (cfg.count("l1_reg"))
+ cerr << setw(25) << "l1 reg " << l1_reg << " '" << cfg["l1_reg"].as<string>() << "'" << endl;
+ if (rescale)
+ cerr << setw(25) << "rescale " << rescale << endl;
+ cerr << setw(25) << "max pairs " << max_pairs << endl;
+ cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
+ cerr << setw(25) << "input " << "'" << input_fn << "'" << endl;
+ cerr << setw(25) << "refs " << "'" << refs_fn << "'" << endl;
+ cerr << setw(25) << "output " << "'" << output_fn << "'" << endl;
+ if (cfg.count("input_weights"))
+ cerr << setw(25) << "weights in " << "'" << cfg["input_weights"].as<string>() << "'" << endl;
+ if (stop_after > 0)
+ cerr << setw(25) << "stop_after " << stop_after << endl;
+ if (!verbose) cerr << "(a dot represents " << DTRAIN_DOTS << " inputs)" << endl;
+ }
+
+
+ for (unsigned t = 0; t < T; t++) // T epochs
+ {
+
+ time_t start, end;
+ time(&start);
+ score_t score_sum = 0.;
+ score_t model_sum(0);
+ unsigned ii = 0, rank_errors = 0, margin_violations = 0, npairs = 0, f_count = 0, list_sz = 0;
+ if (!quiet) cerr << "Iteration #" << t+1 << " of " << T << "." << endl;
+
+ while(true)
+ {
+
+ string in;
+ bool next = false, stop = false; // next iteration or premature stop
+ if (t == 0) {
+ if(!getline(*input, in)) next = true;
+ } else {
+ if (ii == in_sz) next = true; // stop if we reach the end of our input
+ }
+ // stop after X sentences (but still go on for those)
+ if (stop_after > 0 && stop_after == ii && !next) stop = true;
+
+ // produce some pretty output
+ if (!quiet && !verbose) {
+ if (ii == 0) cerr << " ";
+ if ((ii+1) % (DTRAIN_DOTS) == 0) {
+ cerr << ".";
+ cerr.flush();
+ }
+ if ((ii+1) % (20*DTRAIN_DOTS) == 0) {
+ cerr << " " << ii+1 << endl;
+ if (!next && !stop) cerr << " ";
+ }
+ if (stop) {
+ if (ii % (20*DTRAIN_DOTS) != 0) cerr << " " << ii << endl;
+ cerr << "Stopping after " << stop_after << " input sentences." << endl;
+ } else {
+ if (next) {
+ if (ii % (20*DTRAIN_DOTS) != 0) cerr << " " << ii << endl;
+ }
+ }
+ }
+
+ // next iteration
+ if (next || stop) break;
+
+ // weights
+ lambdas.init_vector(&dense_weights);
+
+ // getting input
+ vector<WordID> ref_ids; // reference as vector<WordID>
+ if (t == 0) {
+ string r_;
+ getline(*refs, r_);
+ vector<string> ref_tok;
+ boost::split(ref_tok, r_, boost::is_any_of(" "));
+ register_and_convert(ref_tok, ref_ids);
+ ref_ids_buf.push_back(ref_ids);
+ src_str_buf.push_back(in);
+ } else {
+ ref_ids = ref_ids_buf[ii];
+ }
+ observer->SetRef(ref_ids);
+ if (t == 0)
+ decoder.Decode(in, observer);
+ else
+ decoder.Decode(src_str_buf[ii], observer);
+
+ // get (scored) samples
+ vector<ScoredHyp>* samples = observer->GetSamples();
+
+ if (verbose) {
+ cerr << "--- ref for " << ii << ": ";
+ if (t > 0) printWordIDVec(ref_ids_buf[ii]);
+ else printWordIDVec(ref_ids);
+ cerr << endl;
+ for (unsigned u = 0; u < samples->size(); u++) {
+ cerr << _p2 << _np << "[" << u << ". '";
+ printWordIDVec((*samples)[u].w);
+ cerr << "'" << endl;
+ cerr << "SCORE=" << (*samples)[u].score << ",model="<< (*samples)[u].model << endl;
+ cerr << "F{" << (*samples)[u].f << "} ]" << endl << endl;
+ }
+ }
+
+ score_sum += (*samples)[0].score; // stats for 1best
+ model_sum += (*samples)[0].model;
+
+ f_count += observer->get_f_count();
+ list_sz += observer->get_sz();
+
+ // weight updates
+ if (!noup) {
+ // get pairs
+ vector<pair<ScoredHyp,ScoredHyp> > pairs;
+ if (pair_sampling == "all")
+ all_pairs(samples, pairs, pair_threshold, max_pairs, faster_perceptron);
+ if (pair_sampling == "XYX")
+ partXYX(samples, pairs, pair_threshold, max_pairs, faster_perceptron, hi_lo);
+ if (pair_sampling == "PRO")
+ PROsampling(samples, pairs, pair_threshold, max_pairs);
+ npairs += pairs.size();
+
+ for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
+ it != pairs.end(); it++) {
+ bool rank_error;
+ score_t margin;
+ if (faster_perceptron) { // we only have considering misranked pairs
+ rank_error = true; // pair sampling already did this for us
+ margin = std::numeric_limits<float>::max();
+ } else {
+ rank_error = it->first.model <= it->second.model;
+ margin = fabs(fabs(it->first.model) - fabs(it->second.model));
+ if (!rank_error && margin < loss_margin) margin_violations++;
+ }
+ if (rank_error) rank_errors++;
+ if (scale_bleu_diff) eta = it->first.score - it->second.score;
+ if (rank_error || margin < loss_margin) {
+ SparseVector<weight_t> diff_vec = it->first.f - it->second.f;
+ lambdas.plus_eq_v_times_s(diff_vec, eta);
+ if (gamma)
+ lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs));
+ }
+ }
+
+ // l1 regularization
+ // please note that this penalizes _all_ weights
+ // (contrary to only the ones changed by the last update)
+ // after a _sentence_ (not after each example/pair)
+ if (l1naive) {
+ FastSparseVector<weight_t>::iterator it = lambdas.begin();
+ for (; it != lambdas.end(); ++it) {
+ it->second -= sign(it->second) * l1_reg;
+ }
+ } else if (l1clip) {
+ FastSparseVector<weight_t>::iterator it = lambdas.begin();
+ for (; it != lambdas.end(); ++it) {
+ if (it->second != 0) {
+ weight_t v = it->second;
+ if (v > 0) {
+ it->second = max(0., v - l1_reg);
+ } else {
+ it->second = min(0., v + l1_reg);
+ }
+ }
+ }
+ } else if (l1cumul) {
+ weight_t acc_penalty = (ii+1) * l1_reg; // ii is the index of the current input
+ FastSparseVector<weight_t>::iterator it = lambdas.begin();
+ for (; it != lambdas.end(); ++it) {
+ if (it->second != 0) {
+ weight_t v = it->second;
+ weight_t penalized = 0.;
+ if (v > 0) {
+ penalized = max(0., v-(acc_penalty + cumulative_penalties.get(it->first)));
+ } else {
+ penalized = min(0., v+(acc_penalty - cumulative_penalties.get(it->first)));
+ }
+ it->second = penalized;
+ cumulative_penalties.set_value(it->first, cumulative_penalties.get(it->first)+penalized);
+ }
+ }
+ }
+
+ }
+
+ if (rescale) lambdas /= lambdas.l2norm();
+
+ ++ii;
+
+ } // input loop
+
+ if (average) w_average += lambdas;
+
+ if (scorer_str == "approx_bleu" || scorer_str == "lc_bleu") scorer->Reset();
+
+ if (t == 0) {
+ in_sz = ii; // remember size of input (# lines)
+ }
+
+ // print some stats
+ score_t score_avg = score_sum/(score_t)in_sz;
+ score_t model_avg = model_sum/(score_t)in_sz;
+ score_t score_diff, model_diff;
+ if (t > 0) {
+ score_diff = score_avg - all_scores[t-1].first;
+ model_diff = model_avg - all_scores[t-1].second;
+ } else {
+ score_diff = score_avg;
+ model_diff = model_avg;
+ }
+
+ unsigned nonz = 0;
+ if (!quiet) nonz = (unsigned)lambdas.num_nonzero();
+
+ if (!quiet) {
+ cerr << _p5 << _p << "WEIGHTS" << endl;
+ for (vector<string>::iterator it = print_weights.begin(); it != print_weights.end(); it++) {
+ cerr << setw(18) << *it << " = " << lambdas.get(FD::Convert(*it)) << endl;
+ }
+ cerr << " ---" << endl;
+ cerr << _np << " 1best avg score: " << score_avg;
+ cerr << _p << " (" << score_diff << ")" << endl;
+ cerr << _np << " 1best avg model score: " << model_avg;
+ cerr << _p << " (" << model_diff << ")" << endl;
+ cerr << " avg # pairs: ";
+ cerr << _np << npairs/(float)in_sz;
+ if (faster_perceptron) cerr << " (meaningless)";
+ cerr << endl;
+ cerr << " avg # rank err: ";
+ cerr << rank_errors/(float)in_sz << endl;
+ cerr << " avg # margin viol: ";
+ cerr << margin_violations/(float)in_sz << endl;
+ cerr << " non0 feature count: " << nonz << endl;
+ cerr << " avg list sz: " << list_sz/(float)in_sz << endl;
+ cerr << " avg f count: " << f_count/(float)list_sz << endl;
+ }
+
+ pair<score_t,score_t> remember;
+ remember.first = score_avg;
+ remember.second = model_avg;
+ all_scores.push_back(remember);
+ if (score_avg > max_score) {
+ max_score = score_avg;
+ best_it = t;
+ }
+ time (&end);
+ float time_diff = difftime(end, start);
+ overall_time += time_diff;
+ if (!quiet) {
+ cerr << _p2 << _np << "(time " << time_diff/60. << " min, ";
+ cerr << time_diff/in_sz << " s/S)" << endl;
+ }
+ if (t+1 != T && !quiet) cerr << endl;
+
+ if (noup) break;
+
+ // write weights to file
+ if (select_weights == "best" || keep) {
+ lambdas.init_vector(&dense_weights);
+ string w_fn = "weights." + boost::lexical_cast<string>(t) + ".gz";
+ Weights::WriteToFile(w_fn, dense_weights, true);
+ }
+
+ } // outer loop
+
+ if (average) w_average /= (weight_t)T;
+
+ if (!noup) {
+ if (!quiet) cerr << endl << "Writing weights file to '" << output_fn << "' ..." << endl;
+ if (select_weights == "last" || average) { // last, average
+ WriteFile of(output_fn); // works with '-'
+ ostream& o = *of.stream();
+ o.precision(17);
+ o << _np;
+ if (average) {
+ for (SparseVector<weight_t>::iterator it = w_average.begin(); it != w_average.end(); ++it) {
+ if (it->second == 0) continue;
+ o << FD::Convert(it->first) << '\t' << it->second << endl;
+ }
+ } else {
+ for (SparseVector<weight_t>::iterator it = lambdas.begin(); it != lambdas.end(); ++it) {
+ if (it->second == 0) continue;
+ o << FD::Convert(it->first) << '\t' << it->second << endl;
+ }
+ }
+ } else if (select_weights == "VOID") { // do nothing with the weights
+ } else { // best
+ if (output_fn != "-") {
+ CopyFile("weights."+boost::lexical_cast<string>(best_it)+".gz", output_fn);
+ } else {
+ ReadFile bestw("weights."+boost::lexical_cast<string>(best_it)+".gz");
+ string o;
+ cout.precision(17);
+ cout << _np;
+ while(getline(*bestw, o)) cout << o << endl;
+ }
+ if (!keep) {
+ for (unsigned i = 0; i < T; i++) {
+ string s = "weights." + boost::lexical_cast<string>(i) + ".gz";
+ unlink(s.c_str());
+ }
+ }
+ }
+ if (!quiet) cerr << "done" << endl;
+ }
+
+ if (!quiet) {
+ cerr << _p5 << _np << endl << "---" << endl << "Best iteration: ";
+ cerr << best_it+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl;
+ cerr << "This took " << overall_time/60. << " min." << endl;
+ }
+}
+
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-21 23:09:24 +0000