xp

code/binary_bpr_ablation/BPR_model.py

@@ -151,7 +151,7 @@ class BPRModel(nn.Module):
                     all_decisions = np.concatenate((all_decisions, comp), axis=0)
                                
         mse1 = mean_squared_error(all_labels, all_predictions)
-        print("RMSE", np.sqrt(mse1))#, root_mean_squared_error(all_labels, all_predictions))
+        print("RMSE", np.sqrt(mse1))
         auc = roc_auc_score(all_labels, all_predictions)
         print("AUC:", auc)
         acc = accuracy_score(all_labels, all_decisions)

code/binary_bpr_ablation/main.py

@@ -38,21 +38,18 @@ def read_file(dataTrain, dataTest):
     kc = flattern_arrays(kc.values, kcT.values)
     num_kc = len(kc)
     dico_kc = { k:v for (k,v) in zip(kc, range(len(kc)))}
-    print("NB KC", num_kc)
     # dico users
     users = df['user_id']
     usersT = dfTest['user_id']
     users = flattern_arrays(users.values, usersT.values)
     num_users = len(users)
     dico_users = { k:v for (k,v) in zip(users, range(num_users))}
-    print("NB Users", num_users)
     # dico items and their associated kc
     itemsDT = df['item_id']
     itemsT = dfTest['item_id']
     items = flattern_arrays(itemsDT.values, itemsT.values)
     num_items = len(items)
     dico_items = { k:v for (k,v) in zip(items, range(num_items))} 
-    #print("NB Items", num_items)    
     return dico_kc, dico_users, dico_items
     
 def parse_dataframe(data, dico_kc, dico_users, dico_item, is_train = True):
@@ -79,7 +76,6 @@ def parse_dataframe(data, dico_kc, dico_users, dico_item, is_train = True):
                 col = row['item_id']
                 if col not in dico_items:
                     dico_items[col] = len(dico_items)
-                # Warning, all user's answers are positives!
                 q,r = parse_it(col)
                 col_neg = q+'_'+str(1-int(r))
                 if col_neg not in dico_items: