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-rw-r--r--dtrain/dtrain.cc76
1 files changed, 44 insertions, 32 deletions
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index cf913765..ea5b8835 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -6,35 +6,37 @@ 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("108010"), "how to sample pairs: 'all', '108010' or 'PRO'")
- ("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_")
- ("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")
- ("inc_correct", po::value<bool>()->zero_tokens(), "include correctly ranked pairs into updates")
- ("fselect", po::value<weight_t>()->default_value(-1), "TODO select top x percent of features after each epoch")
+ ("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_")
+ ("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")
+ ("inc_correct", po::value<bool>()->zero_tokens(), "include correctly ranked pairs into updates")
+ ("fselect", po::value<weight_t>()->default_value(-1), "TODO select top x percent of features after each epoch")
+ ("approx_bleu_scale", po::value<score_t>()->default_value(0.9), "scaling for approx. BLEU")
#ifdef DTRAIN_LOCAL
- ("refs,r", po::value<string>(), "references in local mode")
+ ("refs,r", po::value<string>(), "references in local mode")
#endif
- ("noup", po::value<bool>()->zero_tokens(), "do not update weights");
+ ("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")
@@ -71,11 +73,18 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
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>() != "108010" &&
+ 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;
@@ -126,6 +135,7 @@ main(int argc, char** argv)
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>();
bool average = false;
if (select_weights == "avg")
average = true;
@@ -231,6 +241,8 @@ main(int argc, char** argv)
cerr << setw(25) << "learning rate " << eta << endl;
cerr << setw(25) << "gamma " << gamma << 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"))
@@ -400,10 +412,10 @@ main(int argc, char** argv)
vector<pair<ScoredHyp,ScoredHyp> > pairs;
if (pair_sampling == "all")
all_pairs(samples, pairs, pair_threshold);
- if (pair_sampling == "108010")
- part108010(samples, pairs, pair_threshold);
+ if (pair_sampling == "XYX")
+ partXYX(samples, pairs, pair_threshold, hi_lo);
if (pair_sampling == "PRO")
- PROsampling(samples, pairs);
+ PROsampling(samples, pairs, pair_threshold);
npairs += pairs.size();
pair_count += 2*pairs.size();
@@ -456,7 +468,7 @@ main(int argc, char** argv)
}
}
} else if (l1cumul) {
- weight_t acc_penalty = (ii+1) * l1_reg; // Note: ii is the index of the current input
+ 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);
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-11 03:20:50 +0000