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-rw-r--r--dtrain/dtrain.cc75
1 files changed, 40 insertions, 35 deletions
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index 717d47a2..88413a1d 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -6,38 +6,39 @@ 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_")
- ("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), "TODO select top x percent (or by threshold) of features after each epoch")
- ("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")
+ ("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")
+ ("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")
@@ -135,6 +136,7 @@ main(int argc, char** argv)
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;
@@ -167,6 +169,8 @@ main(int argc, char** argv)
scorer = dynamic_cast<SmoothSingleBleuScorer*>(new SmoothSingleBleuScorer);
} 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);
@@ -257,6 +261,7 @@ main(int argc, char** argv)
cerr << setw(25) << "l1 reg " << l1_reg << " '" << cfg["l1_reg"].as<string>() << "'" << endl;
if (rescale)
cerr << setw(25) << "rescale " << rescale << endl;
+ cerr << "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
@@ -421,17 +426,17 @@ main(int argc, char** argv)
// get pairs
vector<pair<ScoredHyp,ScoredHyp> > pairs;
if (pair_sampling == "all")
- all_pairs(samples, pairs, pair_threshold);
+ all_pairs(samples, pairs, pair_threshold, max_pairs);
if (pair_sampling == "XYX")
- partXYX(samples, pairs, pair_threshold, hi_lo);
+ partXYX(samples, pairs, pair_threshold, max_pairs, hi_lo);
if (pair_sampling == "PRO")
- PROsampling(samples, pairs, pair_threshold);
+ 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 filtering already did this for us
+ bool rank_error = true; // pair sampling already did this for us
rank_errors++;
score_t margin = std::numeric_limits<float>::max();
#else
@@ -498,7 +503,7 @@ main(int argc, char** argv)
if (average) w_average += lambdas;
- if (scorer_str == "approx_bleu") scorer->Reset();
+ if (scorer_str == "approx_bleu" || scorer_str == "lc_bleu") scorer->Reset();
if (t == 0) {
in_sz = ii; // remember size of input (# lines)
generated by cgit v1.2.3 (git 2.39.1) at 2024-12-28 03:58:43 +0000