1 files changed, 21 insertions, 15 deletions

diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc
index 441e2cd7..0a27a068 100644
--- a/training/dtrain/dtrain.cc
+++ b/training/dtrain/dtrain.cc
@@ -414,6 +414,12 @@ main(int argc, char** argv)
 
       score_t kbest_loss_first, kbest_loss_last = 0.0;
 
+      for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
+           it != pairs.end(); it++) {
+        score_t model_diff = it->first.model - it->second.model;
+        kbest_loss_first += max(0.0, -1.0 * model_diff);
+      }
+
       for (int ki=0; ki < repeat; ki++) {
 
       score_t kbest_loss = 0.0; // test-k-best
@@ -520,21 +526,22 @@ main(int argc, char** argv)
         }
       }
 
-      if (ki==0) kbest_loss_first = kbest_loss;
       if (ki==repeat-1) { // done
         kbest_loss_last = kbest_loss;
-        score_t best_score = -1.;
-        score_t best_model = -std::numeric_limits<score_t>::max();
-        unsigned best_idx;
-        for (unsigned i=0; i < samples->size(); i++) {
-          score_t s = lambdas.dot((*samples)[i].f);
-          if (s > best_model) {
-            best_idx = i;
-            best_model = s;
+        if (repeat > 1) {
+          score_t best_score = -1.;
+          score_t best_model = -std::numeric_limits<score_t>::max();
+          unsigned best_idx;
+          for (unsigned i=0; i < samples->size(); i++) {
+            score_t s = lambdas.dot((*samples)[i].f);
+            if (s > best_model) {
+              best_idx = i;
+              best_model = s;
+            }
           }
+          score_sum += (*samples)[best_idx].score;
+          model_sum += best_model;
         }
-        score_sum += (*samples)[best_idx].score;
-        model_sum += best_model;
       }
     } // repeat
 
@@ -588,15 +595,14 @@ main(int argc, char** argv)
     cerr << _p << " (" << model_diff << ")" << endl;
     cerr << "           avg # pairs: ";
     cerr << _np << npairs/(float)in_sz << endl;
-    cerr << "     avg # margin viol: ";
-    cerr << margin_violations/(float)in_sz << endl;
     cerr << "        avg # rank err: ";
     cerr << rank_errors/(float)in_sz;
     if (faster_perceptron) cerr << " (meaningless)";
     cerr << endl;
+    cerr << "     avg # margin viol: ";
+    cerr << margin_violations/(float)in_sz << endl;
     if (batch) cerr << "            batch loss: " << batch_loss << endl;
-    if (repeat > 1) cerr << "       k-best loss imp: " << ((float)kbest_loss_improve/in_sz)*100 << "%" << endl;
-
+    cerr << "       k-best loss imp: " << ((float)kbest_loss_improve/in_sz)*100 << "%" << endl;
     cerr << "    non0 feature count: " <<  nonz << endl;
     cerr << "           avg list sz: " << list_sz/(float)in_sz << endl;
     cerr << "           avg f count: " << f_count/(float)list_sz << endl;