added pro stuff,clean up

1 files changed, 64 insertions, 43 deletions

diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index 0853173f..3d3aa2d3 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -6,32 +6,33 @@ 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")
-    ("sample_from",    po::value<string>()->default_value("kbest"),      "where to sample translations from: kbest, forest")
-    ("k",              po::value<unsigned>()->default_value(100),                         "how many translations to sample")
-    ("filter",         po::value<string>()->default_value("unique"),                        "filter kbest list: no, unique")
-    ("pair_sampling",  po::value<string>()->default_value("all"),                  "how to sample pairs: all, rand, 108010")
-    ("N",              po::value<unsigned>()->default_value(3),                                       "N for Ngrams (BLEU)")
-    ("epochs",         po::value<unsigned>()->default_value(2),                             "# of iterations T (per shard)") 
-    ("scorer",         po::value<string>()->default_value("stupid_bleu"),     "scoring: bleu, stupid_*, smooth_*, approx_*")
-    ("stop_after",     po::value<unsigned>()->default_value(0),                              "stop after X input sentences")
-    ("print_weights",  po::value<string>(),                                            "weights to print on each iteration")
-    ("hstreaming",     po::value<string>(),                                "run in hadoop streaming mode, arg is a task id")
-    ("learning_rate",  po::value<weight_t>()->default_value(0.0005),                                        "learning rate")
-    ("gamma",          po::value<weight_t>()->default_value(0),                          "gamma for SVM (0 for perceptron)")
-    ("tmp",            po::value<string>()->default_value("/tmp"),                                        "temp dir to use")
-    ("select_weights", po::value<string>()->default_value("last"), "output 'best' or 'last' weights ('VOID' to throw away)")
-    ("keep_w",         po::value<bool>()->zero_tokens(),                              "protocol weights for each iteration")
-    ("unit_weight_vector", po::value<bool>()->zero_tokens(),                       "Rescale weight vector after each input")
-    ("l1_reg",         po::value<string>()->default_value("no"),         "apply l1 regularization as in Tsuroka et al 2010")
-    ("l1_reg_strength", po::value<weight_t>(),                                                 "l1 regularization strength")
+    ("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")
+    ("sample_from",     po::value<string>()->default_value("kbest"),      "where to sample translations from: kbest, forest")
+    ("k",               po::value<unsigned>()->default_value(100),                         "how many translations to sample")
+    ("filter",          po::value<string>()->default_value("uniq"),                            "filter kbest list: no, uniq")
+    ("pair_sampling",   po::value<string>()->default_value("all"),             "how to sample pairs: all, 5050, 108010, PRO")
+    ("N",               po::value<unsigned>()->default_value(3),                                       "N for Ngrams (BLEU)")
+    ("epochs",          po::value<unsigned>()->default_value(2),                             "# of iterations T (per shard)") 
+    ("scorer",          po::value<string>()->default_value("stupid_bleu"),     "scoring: bleu, stupid_*, smooth_*, approx_*")
+    ("learning_rate",   po::value<weight_t>()->default_value(0.0005),                                        "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' or 'last' weights ('VOID' to throw away)")
+    ("unit_wv",         po::value<bool>()->zero_tokens(),                           "Rescale weight vector after each input")
+    ("l1_reg",          po::value<string>()->default_value("no"),         "apply l1 regularization as in Tsuroka et al 2010")
+    ("l1_reg_strength", po::value<weight_t>(),                                                  "l1 regularization strength")
+    ("update_ok",       po::value<bool>()->zero_tokens(),                      "include correctly ranked pairs into updates")
+    ("stop_after",      po::value<unsigned>()->default_value(0),                              "stop after X input sentences")
+    ("keep_w",          po::value<bool>()->zero_tokens(),                            "keep weights files for each iteration")
+    ("print_weights",   po::value<string>(),                                            "weights to print on each iteration")
+    ("hstreaming",      po::value<string>(),                                "run in hadoop streaming mode, arg is a task id")
+    ("tmp",             po::value<string>()->default_value("/tmp"),                                        "temp dir to use")
 #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")
@@ -63,13 +64,14 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     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>() != "unique"
+  if ((*cfg)["sample_from"].as<string>() == "kbest" && (*cfg)["filter"].as<string>() != "uniq"
        && (*cfg)["filter"].as<string>() != "no") {
-    cerr << "Wrong 'filter' param: '" << (*cfg)["filter"].as<string>() << "', use 'unique' or 'no'." << endl;
+    cerr << "Wrong 'filter' param: '" << (*cfg)["filter"].as<string>() << "', use 'uniq' or 'no'." << endl;
     return false;
   }
   if ((*cfg)["pair_sampling"].as<string>() != "all"
-       && (*cfg)["pair_sampling"].as<string>() != "rand" && (*cfg)["pair_sampling"].as<string>() != "108010") {
+       && (*cfg)["pair_sampling"].as<string>() != "5050" && (*cfg)["pair_sampling"].as<string>() != "108010"
+       && (*cfg)["pair_sampling"].as<string>() != "PRO") {
     cerr << "Wrong 'pair_sampling' param: '" << (*cfg)["pair_sampling"].as<string>() << "', use 'all' or 'rand'." << endl;
     return false;
   }
@@ -101,11 +103,14 @@ main(int argc, char** argv)
     task_id = cfg["hstreaming"].as<string>();
     cerr.precision(17);
   }
-  bool unit_weight_vector = false;
-  if (cfg.count("unit_weight_vector")) unit_weight_vector = true;
+  bool unit_wv = false;
+  if (cfg.count("unit_wv")) unit_wv = true;
   HSReporter rep(task_id);
   bool keep_w = false;
   if (cfg.count("keep_w")) keep_w = true;
+  bool update_ok = false;
+  if (cfg.count("update_ok"))
+    update_ok = true;
 
   const unsigned k = cfg["k"].as<unsigned>();
   const unsigned N = cfg["N"].as<unsigned>(); 
@@ -118,7 +123,7 @@ main(int argc, char** argv)
   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);
@@ -187,7 +192,7 @@ main(int argc, char** argv)
   vector<vector<WordID> > ref_ids_buf; // references as WordID vecs
   // where temp files go
   string tmp_path = cfg["tmp"].as<string>();
-  vector<string> w_tmp_files; // used for protocol_w
+  vector<string> w_tmp_files; // used for keep_w 
 #ifdef DTRAIN_LOCAL
   string refs_fn = cfg["refs"].as<string>();
   ReadFile refs(refs_fn);
@@ -226,6 +231,12 @@ main(int argc, char** argv)
     cerr << setw(25) << "sample from " << "'" << sample_from << "'" << endl;
     cerr << setw(25) << "pairs " << "'" << pair_sampling << "'" << 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 (update_ok)
+      cerr << setw(25) << "up ok " << update_ok << endl;
+    if (unit_wv)
+      cerr << setw(25) << "unit weight vec " << unit_wv << endl;
     if (!verbose) cerr << "(a dot represents " << DTRAIN_DOTS << " lines of input)" << endl;
   }
 
@@ -320,7 +331,7 @@ main(int argc, char** argv)
       // get buffered grammar
       string grammar_str;
       while (true) {
-        string rule;  
+        string rule;
         getline(grammar_buf_in, rule);
         if (boost::starts_with(rule, DTRAIN_GRAMMAR_DELIM)) break;
         grammar_str += rule + "\n";
@@ -372,13 +383,15 @@ main(int argc, char** argv)
     if (!noup) {
       vector<pair<ScoredHyp,ScoredHyp> > pairs;
       if (pair_sampling == "all")
-        sample_all_pairs(samples, pairs);
-      if (pair_sampling == "rand")
-        sample_rand_pairs(samples, pairs, &rng);
+        all_pairs(samples, pairs);
+      if (pair_sampling == "5050")
+        rand_pairs_5050(samples, pairs, &rng);
       if (pair_sampling == "108010")
-        sample108010(samples, pairs);
+        multpart108010(samples, pairs);
+      if (pair_sampling == "PRO")
+        PROsampling(samples, pairs);
       npairs += pairs.size();
-       
+
       for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
            it != pairs.end(); it++) {
         score_t rank_error = it->second.score - it->first.score;
@@ -388,6 +401,11 @@ main(int argc, char** argv)
             SparseVector<weight_t> diff_vec = it->second.f - it->first.f;
             lambdas.plus_eq_v_times_s(diff_vec, eta);
             rank_errors++;
+          } else {
+            if (update_ok) {
+              SparseVector<weight_t> diff_vec = it->first.f - it->second.f;
+              lambdas.plus_eq_v_times_s(diff_vec, eta);
+            }
           }
           if (it->first.model - it->second.model < 1) margin_violations++;
         } else {
@@ -404,6 +422,8 @@ main(int argc, char** argv)
         }
       }
 
+      ////////
+      // TEST THIS
       // reset cumulative_penalties after 1 iter? 
       // do this only once per INPUT (not per pair)
       if (l1naive) {
@@ -439,8 +459,9 @@ main(int argc, char** argv)
         }
       }
     }
+    ////////
 
-    if (unit_weight_vector && sample_from == "forest") lambdas /= lambdas.l2norm();
+    if (unit_wv && sample_from == "forest") lambdas /= lambdas.l2norm();
     
     ++ii;
 
@@ -501,11 +522,11 @@ main(int argc, char** argv)
   }
 
   if (hstreaming) {
-    rep.update_counter("Score 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(score_avg*_SCALE)); 
-    rep.update_counter("Model 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(model_avg*_SCALE)); 
-    rep.update_counter("Pairs avg #"+boost::lexical_cast<string>(t+1), (unsigned)((npairs/(weight_t)in_sz)*_SCALE)); 
-    rep.update_counter("Rank errors avg #"+boost::lexical_cast<string>(t+1), (unsigned)((rank_errors/(weight_t)in_sz)*_SCALE)); 
-    rep.update_counter("Margin violations avg #"+boost::lexical_cast<string>(t+1), (unsigned)((margin_violations/(weight_t)in_sz)*_SCALE)); 
+    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)); 
     unsigned nonz = (unsigned)lambdas.size_nonzero();
     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);