removed hgsampler, more stats, unit_weight_vector arg

1 files changed, 18 insertions, 8 deletions

diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index 5c95c7f1..79047fd9 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -19,12 +19,13 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     ("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>()->default_value("N/A"),          "run in hadoop streaming mode, arg is a task id")
+    ("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")
 #ifdef DTRAIN_LOCAL
     ("refs,r",         po::value<string>(),                                                      "references in local mode")
 #endif
@@ -46,7 +47,7 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
     return false;
   }
   if (cfg->count("hstreaming") && (*cfg)["output"].as<string>() != "-") {
-    cerr << "When using 'hstreaming' the 'output' param should be '-'.";
+    cerr << "When using 'hstreaming' the 'output' param should be '-'." << endl;
     return false;
   }
 #ifdef DTRAIN_LOCAL
@@ -98,6 +99,8 @@ 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;
   HSReporter rep(task_id);
   bool keep_w = false;
   if (cfg.count("keep_w")) keep_w = true;
@@ -226,7 +229,7 @@ main(int argc, char** argv)
 #endif
   score_t score_sum = 0.;
   score_t model_sum(0);
-  unsigned ii = 0, nup = 0, npairs = 0;
+  unsigned ii = 0, rank_errors = 0, margin_violations = 0, npairs = 0;
   if (!quiet) cerr << "Iteration #" << t+1 << " of " << T << "." << endl;
 
   while(true)
@@ -369,21 +372,25 @@ main(int argc, char** argv)
           if (rank_error > 0) {
             SparseVector<weight_t> diff_vec = it->second.f - it->first.f;
             lambdas.plus_eq_v_times_s(diff_vec, eta);
-            nup++;
+            rank_errors++;
           }
+          if (margin < 1) margin_violations++;
         } else {
           // SVM
           score_t margin = it->first.model - it->second.model;
           if (rank_error > 0 || margin < 1) {
             SparseVector<weight_t> diff_vec = it->second.f - it->first.f;
             lambdas.plus_eq_v_times_s(diff_vec, eta);
-            nup++;
+            if (rank_error > 0) rank_errors++;
+            if (margin < 1) margin_violations++;
           }
           // regularization
           lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs));
         }
       }
     }
+
+    if (unit_weight_vector && sample_from == "forest") lambdas /= lambdas.l2norm();
     
     ++ii;
 
@@ -437,15 +444,18 @@ main(int argc, char** argv)
     cerr << _p << " (" << model_diff << ")" << endl;
     cerr << "           avg #pairs: ";
     cerr << _np << npairs/(float)in_sz << endl;
-    cerr << "              avg #up: ";
-    cerr << nup/(float)in_sz << endl;
+    cerr << "        avg #rank err: ";
+    cerr << rank_errors/(float)in_sz << endl;
+    cerr << "     avg #margin viol: ";
+    cerr << margin_violations/float(in_sz) << endl;
   }
 
   if (hstreaming) {
     rep.update_counter("Score 1best avg #"+boost::lexical_cast<string>(t+1), score_avg); 
     rep.update_counter("Model 1best avg #"+boost::lexical_cast<string>(t+1), model_avg); 
     rep.update_counter("Pairs avg #"+boost::lexical_cast<string>(t+1), npairs/(weight_t)in_sz); 
-    rep.update_counter("Updates avg #"+boost::lexical_cast<string>(t+1), nup/(weight_t)in_sz); 
+    rep.update_counter("Rank errors avg #"+boost::lexical_cast<string>(t+1), rank_errors/(weight_t)in_sz); 
+    rep.update_counter("Margin violations avg #"+boost::lexical_cast<string>(t+1), margin_violations/(weight_t)in_sz); 
     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);