made pair sampling configurable

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);