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#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")
("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")
("tmp", po::value<string>()->default_value("/tmp"), "temp dir to use")
("keep", po::value<bool>()->zero_tokens(), "keep weights files for each iteration")
("hstreaming", po::value<string>(), "run in hadoop streaming mode, arg is a task id")
("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(0.0001), "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)")
("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 IMPL") // 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.")
#ifdef DTRAIN_LOCAL
("refs,r", po::value<string>(), "references in local mode")
#endif
("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->count("hstreaming") && (*cfg)["output"].as<string>() != "-") {
cerr << "When using 'hstreaming' the 'output' param should be '-'." << endl;
return false;
}
#ifdef DTRAIN_LOCAL
if ((*cfg)["input"].as<string>() == "-") {
cerr << "Can't use stdin as input with this binary. Recompile without DTRAIN_LOCAL" << endl;
return false;
}
#endif
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 hstreaming = false;
string task_id;
if (cfg.count("hstreaming")) {
hstreaming = true;
quiet = true;
task_id = cfg["hstreaming"].as<string>();
cerr.precision(17);
}
bool rescale = false;
if (cfg.count("rescale")) rescale = true;
HSReporter rep(task_id);
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 == "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>();
// 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
// where temp files go
string tmp_path = cfg["tmp"].as<string>();
#ifdef DTRAIN_LOCAL
string refs_fn = cfg["refs"].as<string>();
ReadFile refs(refs_fn);
#else
string grammar_buf_fn = gettmpf(tmp_path, "dtrain-grammars");
ogzstream grammar_buf_out;
grammar_buf_out.open(grammar_buf_fn.c_str());
#endif
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(25) << "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) << "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;
#ifdef DTRAIN_LOCAL
cerr << setw(25) << "refs " << "'" << refs_fn << "'" << endl;
#endif
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
{
if (hstreaming) cerr << "reporter:status:Iteration #" << t+1 << " of " << T << endl;
time_t start, end;
time(&start);
#ifndef DTRAIN_LOCAL
igzstream grammar_buf_in;
if (t > 0) grammar_buf_in.open(grammar_buf_fn.c_str());
#endif
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>
#ifndef DTRAIN_LOCAL
vector<string> in_split; // input: sid\tsrc\tref\tpsg
if (t == 0) {
// handling input
split_in(in, in_split);
if (hstreaming && ii == 0) cerr << "reporter:counter:" << task_id << ",First ID," << in_split[0] << endl;
// getting reference
vector<string> ref_tok;
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; // ignore broken grammars
for (string::iterator it = in.begin(); it != in.end(); it++) {
if (!isspace(*it)) {
broken_grammar = false;
break;
}
}
if (broken_grammar) {
cerr << "Broken grammar for " << ii+1 << "! Ignoring this input." << endl;
continue;
}
boost::replace_all(in, "\t", "\n");
in += "\n";
grammar_buf_out << in << DTRAIN_GRAMMAR_DELIM << " " << in_split[0] << endl;
decoder.SetSentenceGrammarFromString(in);
src_str_buf.push_back(in_split[1]);
// decode
observer->SetRef(ref_ids);
decoder.Decode(in_split[1], observer);
} else {
// get buffered grammar
string grammar_str;
while (true) {
string rule;
getline(grammar_buf_in, rule);
if (boost::starts_with(rule, DTRAIN_GRAMMAR_DELIM)) break;
grammar_str += rule + "\n";
}
decoder.SetSentenceGrammarFromString(grammar_str);
// decode
observer->SetRef(ref_ids_buf[ii]);
decoder.Decode(src_str_buf[ii], observer);
}
#else
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);
#endif
// 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);
if (pair_sampling == "XYX")
partXYX(samples, pairs, pair_threshold, max_pairs, 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++) {
#ifdef DTRAIN_FASTER_PERCEPTRON
bool rank_error = true; // pair sampling already did this for us
rank_errors++;
score_t margin = std::numeric_limits<float>::max();
#else
bool rank_error = it->first.model <= it->second.model;
if (rank_error) rank_errors++;
score_t margin = fabs(fabs(it->first.model) - fabs(it->second.model));
if (!rank_error && margin < loss_margin) margin_violations++;
#endif
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
if (l1naive) {
for (unsigned d = 0; d < lambdas.size(); d++) {
weight_t v = lambdas.get(d);
lambdas.set_value(d, v - sign(v) * l1_reg);
}
} else if (l1clip) {
for (unsigned d = 0; d < lambdas.size(); d++) {
if (lambdas.nonzero(d)) {
weight_t v = lambdas.get(d);
if (v > 0) {
lambdas.set_value(d, max(0., v - l1_reg));
} else {
lambdas.set_value(d, min(0., v + l1_reg));
}
}
}
} else if (l1cumul) {
weight_t acc_penalty = (ii+1) * l1_reg; // ii is the index of the current input
for (unsigned d = 0; d < lambdas.size(); d++) {
if (lambdas.nonzero(d)) {
weight_t v = lambdas.get(d);
weight_t penalty = 0;
if (v > 0) {
penalty = max(0., v-(acc_penalty + cumulative_penalties.get(d)));
} else {
penalty = min(0., v+(acc_penalty - cumulative_penalties.get(d)));
}
lambdas.set_value(d, penalty);
cumulative_penalties.set_value(d, cumulative_penalties.get(d)+penalty);
}
}
}
}
if (rescale) lambdas /= lambdas.l2norm();
++ii;
if (hstreaming) {
rep.update_counter("Seen #"+boost::lexical_cast<string>(t+1), 1u);
rep.update_counter("Seen", 1u);
}
} // 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)
if (hstreaming) {
rep.update_counter("|Input|", ii);
rep.update_gcounter("|Input|", ii);
rep.update_gcounter("Shards", 1u);
}
}
#ifndef DTRAIN_LOCAL
if (t == 0) {
grammar_buf_out.close();
} else {
grammar_buf_in.close();
}
#endif
// 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 || hstreaming) 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 << endl;
cerr << " avg # rank err: ";
cerr << rank_errors/(float)in_sz << endl;
#ifndef DTRAIN_FASTER_PERCEPTRON
cerr << " avg # margin viol: ";
cerr << margin_violations/(float)in_sz << endl;
#endif
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;
}
if (hstreaming) {
rep.update_counter("Score 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(score_avg*DTRAIN_SCALE));
rep.update_counter("Model 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(model_avg*DTRAIN_SCALE));
rep.update_counter("Pairs avg #"+boost::lexical_cast<string>(t+1), (unsigned)((npairs/(weight_t)in_sz)*DTRAIN_SCALE));
rep.update_counter("Rank errors avg #"+boost::lexical_cast<string>(t+1), (unsigned)((rank_errors/(weight_t)in_sz)*DTRAIN_SCALE));
rep.update_counter("Margin violations avg #"+boost::lexical_cast<string>(t+1), (unsigned)((margin_violations/(weight_t)in_sz)*DTRAIN_SCALE));
rep.update_counter("Non zero feature count #"+boost::lexical_cast<string>(t+1), nonz);
rep.update_gcounter("Non zero feature count #"+boost::lexical_cast<string>(t+1), nonz);
}
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;
#ifndef DTRAIN_LOCAL
unlink(grammar_buf_fn.c_str());
#endif
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>::const_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>::const_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 (output_fn == "-" && hstreaming) cout << "__SHARD_COUNT__\t1" << endl;
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;
}
}
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