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-rw-r--r--dtrain/dtrain.cc390
1 files changed, 186 insertions, 204 deletions
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index 76fdb49c..a70ca2f1 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -4,64 +4,66 @@
bool
dtrain_init(int argc, char** argv, po::variables_map* cfg)
{
- po::options_description conff("Configuration File Options");
- conff.add_options()
- ("decoder_config", po::value<string>(), "configuration file for cdec")
- ("kbest", po::value<size_t>()->default_value(100), "k for kbest")
- ("ngrams", po::value<size_t>()->default_value(3), "N for Ngrams")
- ("filter", po::value<string>()->default_value("unique"), "filter kbest list")
- ("epochs", po::value<size_t>()->default_value(2), "# of iterations T")
- ("input", po::value<string>()->default_value("-"), "input file")
- ("output", po::value<string>()->default_value("-"), "output weights file")
- ("scorer", po::value<string>()->default_value("stupid_bleu"), "scoring metric")
- ("stop_after", po::value<size_t>()->default_value(0), "stop after X input sentences")
- ("input_weights", po::value<string>(), "input weights file (e.g. from previous iteration)")
- ("wprint", po::value<string>(), "weights to print on each iteration")
- ("hstreaming", po::value<bool>()->zero_tokens(), "run in hadoop streaming mode")
- ("noup", po::value<bool>()->zero_tokens(), "do not update weights");
-
- po::options_description clo("Command Line Options");
- clo.add_options()
+ po::options_description ini("Configuration File Options");
+ ini.add_options()
+ ("input", po::value<string>()->default_value("-"), "input file")
+ ("output", po::value<string>()->default_value("-"), "output weights file (or VOID)")
+ ("input_weights", po::value<string>(), "input weights file (e.g. from previous iteration)")
+ ("decoder_config", po::value<string>(), "configuration file for cdec")
+ ("ksamples", po::value<size_t>()->default_value(100), "size of kbest or sample from forest")
+ ("sample_from", po::value<string>()->default_value("kbest"), "where to get translations from")
+ ("filter", po::value<string>()->default_value("unique"), "filter kbest list")
+ ("pair_sampling", po::value<string>()->default_value("all"), "how to sample pairs: all, rand")
+ ("ngrams", po::value<size_t>()->default_value(3), "N for Ngrams")
+ ("epochs", po::value<size_t>()->default_value(2), "# of iterations T")
+ ("scorer", po::value<string>()->default_value("stupid_bleu"), "scoring metric")
+ ("stop_after", po::value<size_t>()->default_value(0), "stop after X input sentences")
+ ("print_weights", po::value<string>(), "weights to print on each iteration")
+ ("hstreaming", po::value<bool>()->zero_tokens(), "run in hadoop streaming mode")
+ ("learning_rate", po::value<double>()->default_value(0.0005), "learning rate")
+ ("gamma", po::value<double>()->default_value(0.), "gamma for SVM (0 for perceptron)")
+ ("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");
- 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);
+ cl.add(ini);
+ po::store(parse_command_line(argc, argv, cl), *cfg);
if (cfg->count("config")) {
- ifstream config((*cfg)["config"].as<string>().c_str());
- po::store(po::parse_config_file(config, config_options), *cfg);
+ 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 << cmdline_options << endl;
+ cerr << cl << endl;
return false;
}
if (cfg->count("hstreaming") && (*cfg)["output"].as<string>() != "-") {
cerr << "When using 'hstreaming' the 'output' param should be '-'.";
return false;
}
- if (cfg->count("filter") && (*cfg)["filter"].as<string>() != "unique"
+ if ((*cfg)["filter"].as<string>() != "unique"
&& (*cfg)["filter"].as<string>() != "no") {
- cerr << "Wrong 'filter' type: '" << (*cfg)["filter"].as<string>() << "'." << endl;
+ cerr << "Wrong 'filter' param: '" << (*cfg)["filter"].as<string>() << "', use 'unique' or 'no'." << endl;
+ }
+ if ((*cfg)["pair_sampling"].as<string>() != "all"
+ && (*cfg)["pair_sampling"].as<string>() != "rand") {
+ cerr << "Wrong 'pair_sampling' param: '" << (*cfg)["pair_sampling"].as<string>() << "', use 'all' or 'rand'." << endl;
+ }
+ 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 true;
}
-#include "filelib.h"
-
int
main(int argc, char** argv)
{
- cout << _p5;
// handle most parameters
po::variables_map cfg;
- if (! dtrain_init(argc, argv, &cfg)) exit(1); // something is wrong
+ if (!dtrain_init(argc, argv, &cfg)) exit(1); // something is wrong
bool quiet = false;
if (cfg.count("quiet")) quiet = true;
bool verbose = false;
@@ -73,43 +75,37 @@ main(int argc, char** argv)
hstreaming = true;
quiet = true;
}
- const size_t k = cfg["kbest"].as<size_t>();
+ const size_t k = cfg["ksamples"].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>();
- 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("input_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(" "));
- }
+ const string sample_from = cfg["sample_from"].as<string>();
+ const string pair_sampling = cfg["pair_sampling"].as<string>();
+ vector<string> print_weights;
+ if (cfg.count("print_weights"))
+ boost::split(print_weights, cfg["print_weights"].as<string>(), boost::is_any_of(" "));
- // setup decoder, observer
+ // setup decoder
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);
+
+ MT19937 rng; // random number generator
+ // setup decoder observer
+ HypoSampler* observer;
+ if (sample_from == "kbest") {
+ observer = dynamic_cast<KBestGetter*>(new KBestGetter(k, filter_type));
+ } else {
+ observer = dynamic_cast<KSampler*>(new KSampler(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>);
+ score_t (*scorer)(NgramCounts&, const size_t, const size_t, size_t, vector<score_t>);
if (scorer_str == "bleu") {
scorer = &bleu;
} else if (scorer_str == "stupid_bleu") {
@@ -122,58 +118,64 @@ main(int argc, char** argv)
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?
+ size_t global_hyp_len = 0; // sum hypothesis lengths
+ size_t global_ref_len = 0; // sum reference lengths
+ // ^^^ global_* for approx_bleu
+ vector<score_t> bleu_weights; // we leave this empty -> 1/N
if (!quiet) cout << setw(26) << "scorer '" << scorer_str << "'" << endl << endl;
// init weights
Weights weights;
- if (cfg.count("weights")) weights.InitFromFile(cfg["weights"].as<string>());
+ if (cfg.count("input_weights")) weights.InitFromFile(cfg["input_weights"].as<string>());
SparseVector<double> lambdas;
weights.InitSparseVector(&lambdas);
vector<double> dense_weights;
+ // meta params for perceptron, SVM
+ double eta = cfg["learning_rate"].as<double>();
+ double gamma = cfg["gamma"].as<double>();
+ lambdas.add_value(FD::Convert("__bias"), 0);
+
// 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
-
- // buffer input for t > 0
- vector<string> src_str_buf; // source strings, TODO? memory
- vector<vector<WordID> > ref_ids_buf; // references as WordID vecs
+ ReadFile input(input_fn);
+ // buffer input for t > 0
+ vector<string> src_str_buf; // source strings
+ vector<vector<WordID> > ref_ids_buf; // references as WordID vecs
// this is for writing the grammar buffer file
char grammar_buf_fn[] = DTRAIN_TMP_DIR"/dtrain-grammars-XXXXXX";
mkstemp(grammar_buf_fn);
ogzstream grammar_buf_out;
grammar_buf_out.open(grammar_buf_fn);
- 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));
+ size_t in_sz = 999999999; // input index, input size
+ vector<pair<score_t,score_t> > all_scores;
+ score_t max_score = 0.;
+ size_t best_it = 0;
+ float overall_time = 0.;
+
+ // output cfg
+ if (!quiet) {
+ cout << _p5;
+ 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("input_weights"))
+ cout << setw(25) << "weights in" << cfg["input_weights"].as<string>() << endl;
+ cout << setw(25) << "input " << "'" << cfg["input"].as<string>() << "'" << endl;
+ cout << setw(25) << "output " << "'" << cfg["output"].as<string>() << "'" << endl;
+ if (sample_from == "kbest")
+ cout << setw(25) << "filter " << "'" << filter_type << "'" << endl;
+ cout << setw(25) << "learning rate " << eta << endl;
+ cout << setw(25) << "gamma " << gamma << endl;
+ cout << setw(25) << "sample from " << "'" << sample_from << "'" << endl;
+ cout << setw(25) << "pairs " << "'" << pair_sampling << "'" << endl;
+ if (!verbose) cout << "(a dot represents " << DTRAIN_DOTS << " lines of input)" << endl;
+ }
for (size_t t = 0; t < T; t++) // T epochs
@@ -181,58 +183,44 @@ main(int argc, char** argv)
time_t start, end;
time(&start);
-
- // actually, we need only need this if t > 0 FIXME
igzstream grammar_buf_in;
if (t > 0) grammar_buf_in.open(grammar_buf_fn);
-
- // reset average scores
- acc_1best_score = acc_1best_model = 0.;
-
- // reset sentence counter
- sid = 0;
-
+ score_t score_sum = 0., model_sum = 0.;
+ size_t ii = 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;
- }
+ string in;
+ bool next = false, stop = false; // next iteration or premature stop
+ if (t == 0) {
+ if(!getline(*input, in)) next = true;
} else {
- if (sid == in_sz) next = true; // stop if we reach the end of our input
+ if (ii == 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;
+ if (stop_after > 0 && stop_after == ii && !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;
- }
- }
+ if (ii == 0) cout << " ";
+ if ((ii+1) % (DTRAIN_DOTS) == 0) {
+ cout << ".";
+ cout.flush();
+ }
+ if ((ii+1) % (20*DTRAIN_DOTS) == 0) {
+ cout << " " << ii+1 << endl;
+ if (!next && !stop) cout << " ";
+ }
+ if (stop) {
+ if (ii % (20*DTRAIN_DOTS) != 0) cout << " " << ii << endl;
+ cout << "Stopping after " << stop_after << " input sentences." << endl;
+ } else {
+ if (next) {
+ if (ii % (20*DTRAIN_DOTS) != 0) cout << " " << ii << endl;
}
+ }
}
// next iteration
@@ -244,12 +232,15 @@ main(int argc, char** argv)
weights.InitVector(&dense_weights);
decoder.SetWeights(dense_weights);
+ // getting input
+ vector<string> in_split; // input: sid\tsrc\tref\tpsg
+ vector<WordID> ref_ids; // reference as vector<WordID>
if (t == 0) {
// handling input
- in_split.clear();
strsplit(in, in_split, '\t', 4);
// getting reference
- ref_tok.clear(); ref_ids.clear();
+ ref_ids.clear();
+ vector<string> ref_tok;
strsplit(in_split[2], ref_tok, ' ');
register_and_convert(ref_tok, ref_ids);
ref_ids_buf.push_back(ref_ids);
@@ -268,7 +259,7 @@ main(int argc, char** argv)
decoder.SetSentenceGrammarFromString(in_split[3]);
// decode
src_str_buf.push_back(in_split[1]);
- decoder.Decode(in_split[1], &observer);
+ decoder.Decode(in_split[1], observer);
} else {
// get buffered grammar
string grammar_str;
@@ -280,73 +271,67 @@ main(int argc, char** argv)
}
decoder.SetSentenceGrammarFromString(grammar_str);
// decode
- decoder.Decode(src_str_buf[sid], &observer);
+ decoder.Decode(src_str_buf[ii], observer);
}
- // get kbest list
- KBestList* kb;
- //if () { // TODO get from forest
- kb = observer.GetKBest();
- //}
+ Samples* samples = observer->GetSamples();
// (local) scoring
- 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);
+ if (t > 0) ref_ids = ref_ids_buf[ii];
+ score_t score = 0.;
+ for (size_t i = 0; i < samples->GetSize(); i++) {
+ NgramCounts counts = make_ngram_counts(ref_ids, samples->sents[i], N);
if (scorer_str == "approx_bleu") {
+ size_t hyp_len = 0;
if (i == 0) { // 'context of 1best translations'
global_counts += counts;
- global_hyp_len += kb->sents[i].size();
+ global_hyp_len += samples->sents[i].size();
global_ref_len += ref_ids.size();
counts.reset();
- cand_len = 0;
} else {
- cand_len = kb->sents[i].size();
+ hyp_len = samples->sents[i].size();
}
NgramCounts counts_tmp = global_counts + counts;
- score = .9*scorer(counts_tmp,
- global_ref_len,
- global_hyp_len + cand_len, N, bleu_weights);
+ score = .9 * scorer(counts_tmp,
+ global_ref_len,
+ global_hyp_len + hyp_len, N, bleu_weights);
} else {
- cand_len = kb->sents[i].size();
score = scorer(counts,
- ref_ids.size(),
- kb->sents[i].size(), N, bleu_weights);
+ ref_ids.size(),
+ samples->sents[i].size(), N, bleu_weights);
}
- kb->scores.push_back(score);
+ samples->scores.push_back(score);
if (i == 0) {
- acc_1best_score += score;
- acc_1best_model += kb->model_scores[i];
+ score_sum += score;
+ model_sum += samples->model_scores[i];
}
if (verbose) {
if (i == 0) cout << "'" << TD::GetString(ref_ids) << "' [ref]" << endl;
- cout << _p5 << _np << "[hyp " << i << "] " << "'" << TD::GetString(kb->sents[i]) << "'";
- cout << " [SCORE=" << score << ",model="<< kb->model_scores[i] << "]" << endl;
- //cout << kb->feats[i] << endl; // too verbose
+ cout << _p5 << _np << "[hyp " << i << "] " << "'" << TD::GetString(samples->sents[i]) << "'";
+ cout << " [SCORE=" << score << ",model="<< samples->model_scores[i] << "]" << endl;
+ cout << samples->feats[i] << endl;
}
- } // Nbest loop
+ } // sample/scoring loop
if (verbose) cout << endl;
//////////////////////////////////////////////////////////
// UPDATE WEIGHTS
if (!noup) {
-
- int up = 0;
-
- TrainingInstances pairs;
- sample_all_pairs(kb, pairs);
- //sample_rand_pairs(kb, pairs, &rng);
+ vector<Pair> pairs;
+ if (pair_sampling == "all")
+ sample_all_pairs(samples, pairs);
+ if (pair_sampling == "rand")
+ sample_rand_pairs(samples, pairs, &rng);
- for (TrainingInstances::iterator ti = pairs.begin();
+ for (vector<Pair>::iterator ti = pairs.begin();
ti != pairs.end(); ti++) {
SparseVector<double> dv;
if (ti->first_score - ti->second_score < 0) {
- up++;
dv = ti->second - ti->first;
//} else {
//dv = ti->first - ti->second;
@@ -360,10 +345,10 @@ main(int argc, char** argv)
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 << " i " << TD::GetString(samples->sents[ti->first_rank]) << endl;
+ cout << " " << samples->feats[ti->first_rank] << endl;
+ cout << " j " << TD::GetString(samples->sents[ti->second_rank]) << endl;
+ cout << " " << samples->feats[ti->second_rank] << endl;
cout << " diff vec: " << dv << endl;
cout << " lambdas after update: " << lambdas << endl;
cout << "}}" << endl;
@@ -378,69 +363,66 @@ main(int argc, char** argv)
//double l2 = lambdas.l2norm();
//if (l2) lambdas /= lambdas.l2norm();
- //cout << up << endl;
}
//////////////////////////////////////////////////////////
- ++sid;
+ ++ii;
- if (hstreaming) cerr << "reporter:counter:dtrain,sid," << sid << endl;
+ if (hstreaming) cerr << "reporter:counter:dtrain,sid," << in_split[0] << endl;
} // input loop
if (t == 0) {
- in_sz = sid; // remember size (lines) of input
+ in_sz = ii; // remember size of input (# lines)
grammar_buf_out.close();
- if (input_fn != "-") input.close();
} else {
grammar_buf_in.close();
}
// 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;
+ 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) {
- 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];
+ score_diff = score_avg - all_scores[t-1].first;
+ model_diff = model_avg - all_scores[t-1].second;
} else {
- avg_1best_score_diff = avg_1best_score;
- avg_1best_model_diff = avg_1best_model;
+ score_diff = score_avg;
+ model_diff = model_avg;
}
if (!quiet) {
cout << _p5 << _p << "WEIGHTS" << endl;
- for (vector<string>::iterator it = wprint.begin(); it != wprint.end(); it++) {
- cout << setw(16) << *it << " = " << dense_weights[FD::Convert(*it)] << endl;
+ for (vector<string>::iterator it = print_weights.begin(); it != print_weights.end(); it++) {
+ cout << setw(16) << *it << " = " << lambdas.get(FD::Convert(*it)) << endl;
}
cout << " ---" << endl;
- cout << _np << " avg score: " << avg_1best_score;
- cout << _p << " (" << avg_1best_score_diff << ")" << endl;
- cout << _np << "avg model score: " << avg_1best_model;
- cout << _p << " (" << avg_1best_model_diff << ")" << endl;
+ cout << _np << " 1best avg score: " << score_avg;
+ cout << _p << " (" << score_diff << ")" << endl;
+ cout << _np << "1best avg model score: " << model_avg;
+ cout << _p << " (" << 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;
+ 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);
- double time_dif = difftime(end, start);
- overall_time += time_dif;
+ float time_diff = difftime(end, start);
+ overall_time += time_diff;
if (!quiet) {
- cout << _p2 << _np << "(time " << time_dif/60. << " min, ";
- cout << time_dif/(double)in_sz<< " s/S)" << endl;
+ cout << _p2 << _np << "(time " << time_diff/60. << " min, ";
+ cout << time_diff/(float)in_sz<< " s/S)" << endl;
}
-
if (t+1 != T && !quiet) cout << endl;
if (noup) break;
} // outer loop
- //unlink(grammar_buf_fn);
+ unlink(grammar_buf_fn);
if (!noup) {
if (!quiet) cout << endl << "writing weights file '" << cfg["output"].as<string>() << "' ...";
@@ -452,7 +434,7 @@ main(int argc, char** argv)
cout << _np << FD::Convert(ti->first) << "\t" << ti->second << endl;
}
if (hstreaming) cout << "__SHARD_COUNT__\t1" << endl;
- } else {
+ } else if (cfg["output"].as<string>() != "VOID") {
weights.InitFromVector(lambdas);
weights.WriteToFile(cfg["output"].as<string>(), true);
}
@@ -461,7 +443,7 @@ main(int argc, char** argv)
if (!quiet) {
cout << _p5 << _np << endl << "---" << endl << "Best iteration: ";
- cout << best_t+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl;
+ cout << best_it+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl;
cout << _p2 << "This took " << overall_time/60. << " min." << endl;
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-15 08:18:25 +0000