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#include "dtrain.h"
#include "sample.h"
#include "score.h"
#include "update.h"
using namespace dtrain;
int
main(int argc, char** argv)
{
// get configuration
po::variables_map conf;
if (!dtrain_init(argc, argv, &conf))
exit(1); // something is wrong
const size_t k = conf["k"].as<size_t>();
const size_t N = conf["N"].as<size_t>();
const size_t T = conf["iterations"].as<size_t>();
const weight_t eta = conf["learning_rate"].as<weight_t>();
const weight_t margin = conf["margin"].as<weight_t>();
const bool average = conf["average"].as<bool>();
const weight_t l1_reg = conf["l1_reg"].as<weight_t>();
const bool keep = conf["keep"].as<bool>();
const string output_fn = conf["output"].as<string>();
vector<string> print_weights;
boost::split(print_weights, conf["print_weights"].as<string>(),
boost::is_any_of(" "));
// setup decoder
register_feature_functions();
SetSilent(true);
ReadFile f(conf["decoder_conf"].as<string>());
Decoder decoder(f.stream());
ScoredKbest* observer = new ScoredKbest(k, new PerSentenceBleuScorer(N));
// weights
vector<weight_t>& decoder_weights = decoder.CurrentWeightVector();
SparseVector<weight_t> lambdas, w_average;
if (conf.count("input_weights")) {
Weights::InitFromFile(conf["input_weights"].as<string>(), &decoder_weights);
Weights::InitSparseVector(decoder_weights, &lambdas);
}
// input
string input_fn = conf["bitext"].as<string>();
ReadFile input(input_fn);
vector<string> buf; // decoder only accepts strings as input
vector<vector<Ngrams> > buf_ngs; // compute ngrams and lengths of references
vector<vector<size_t> > buf_ls; // just once
size_t input_sz = 0;
cerr << _p4;
// output configuration
cerr << "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) << "learning rate " << eta << endl;
cerr << setw(25) << "margin " << margin << endl;
cerr << setw(25) << "l1 reg " << l1_reg << endl;
cerr << setw(25) << "decoder conf " << "'"
<< conf["decoder_conf"].as<string>() << "'" << endl;
cerr << setw(25) << "input " << "'" << input_fn << "'" << endl;
cerr << setw(25) << "output " << "'" << output_fn << "'" << endl;
if (conf.count("input_weights")) {
cerr << setw(25) << "weights in " << "'"
<< conf["input_weights"].as<string>() << "'" << endl;
}
cerr << "(1 dot per processed input)" << endl;
// meta
weight_t best=0., gold_prev=0.;
size_t best_iteration = 0;
time_t total_time = 0.;
for (size_t t = 0; t < T; t++) // T iterations
{
time_t start, end;
time(&start);
weight_t gold_sum=0., model_sum=0.;
size_t i=0, num_up=0, feature_count=0, list_sz=0;
cerr << "Iteration #" << t+1 << " of " << T << "." << endl;
while(true)
{
bool next = true;
// getting input
if (t == 0) {
string in;
if(!getline(*input, in)) {
next = false;
} else {
vector<string> parts;
boost::algorithm::split_regex(parts, in, boost::regex(" \\|\\|\\| "));
buf.push_back(parts[0]);
parts.erase(parts.begin());
buf_ngs.push_back({});
buf_ls.push_back({});
for (auto s: parts) {
vector<WordID> r;
vector<string> toks;
boost::split(toks, s, boost::is_any_of(" "));
for (auto tok: toks)
r.push_back(TD::Convert(tok));
buf_ngs.back().emplace_back(MakeNgrams(r, N));
buf_ls.back().push_back(r.size());
}
}
} else {
next = i<input_sz;
}
// produce some pretty output
if (next) {
if (i%20 == 0)
cerr << " ";
cerr << ".";
if ((i+1)%20==0)
cerr << " " << i+1 << endl;
} else {
if (i%20 != 0)
cerr << " " << i << endl;
}
cerr.flush();
// stop iterating
if (!next) break;
// decode
if (t > 0 || i > 0)
lambdas.init_vector(&decoder_weights);
observer->SetReference(buf_ngs[i], buf_ls[i]);
decoder.Decode(buf[i], observer);
vector<ScoredHyp>* samples = observer->GetSamples();
// stats for 1best
gold_sum += samples->front().gold;
model_sum += samples->front().model;
feature_count += observer->GetFeatureCount();
list_sz += observer->GetSize();
// get pairs and update
SparseVector<weight_t> updates;
num_up += CollectUpdates(samples, updates, margin);
SparseVector<weight_t> lambdas_copy;
if (l1_reg)
lambdas_copy = lambdas;
lambdas.plus_eq_v_times_s(updates, eta);
// l1 regularization
// NB: regularization is done after each sentence,
// not after every single pair!
if (l1_reg) {
SparseVector<weight_t>::iterator it = lambdas.begin();
for (; it != lambdas.end(); ++it) {
weight_t v = it->second;
if (!v)
continue;
if (!lambdas_copy.get(it->first) // new or..
|| lambdas_copy.get(it->first)!=v) // updated feature
{
if (v > 0) {
it->second = max(0., v - l1_reg);
} else {
it->second = min(0., v + l1_reg);
}
}
}
}
i++;
} // input loop
if (t == 0)
input_sz = i; // remember size of input (# lines)
// update average
if (average)
w_average += lambdas;
// stats
weight_t gold_avg = gold_sum/(weight_t)input_sz;
cerr << _p << "WEIGHTS" << endl;
for (auto name: print_weights)
cerr << setw(18) << name << " = " << lambdas.get(FD::Convert(name)) << endl;
cerr << " ---" << endl;
cerr << _np << " 1best avg score: " << gold_avg*100;
cerr << _p << " (" << (gold_avg-gold_prev)*100 << ")" << endl;
cerr << " 1best avg model score: "
<< model_sum/(weight_t)input_sz << endl;
cerr << " avg # updates: ";
cerr << _np << num_up/(float)input_sz << endl;
cerr << " non-0 feature count: " << lambdas.num_nonzero() << endl;
cerr << " avg f count: " << feature_count/(float)list_sz << endl;
cerr << " avg list sz: " << list_sz/(float)input_sz << endl;
if (gold_avg > best) {
best = gold_avg;
best_iteration = t;
}
gold_prev = gold_avg;
time (&end);
time_t time_diff = difftime(end, start);
total_time += time_diff;
cerr << "(time " << time_diff/60. << " min, ";
cerr << time_diff/input_sz << " s/S)" << endl;
if (t+1 != T) cerr << endl;
if (keep) { // keep intermediate weights
lambdas.init_vector(&decoder_weights);
string w_fn = "weights." + boost::lexical_cast<string>(t) + ".gz";
Weights::WriteToFile(w_fn, decoder_weights, true);
}
} // outer loop
// final weights
if (average) {
w_average /= T;
w_average.init_vector(decoder_weights);
} else if (!keep) {
lambdas.init_vector(decoder_weights);
}
if (average || !keep)
Weights::WriteToFile(output_fn, decoder_weights, true);
cerr << endl << "---" << endl << "Best iteration: ";
cerr << best_iteration+1 << " [GOLD = " << best*100 << "]." << endl;
cerr << "This took " << total_time/60. << " min." << endl;
return 0;
}
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