Update predefined.py

predefined.py

@@ -488,19 +488,19 @@ def get_output_new(w,wt,I_b=n_Identities,B_b=n_Behaviors,M_b=n_Modifiers,batch_s
             df_identity=pd.concat([Identities,new_df],axis=0)
             df2=out_df(data=q,predictions=preds,df_ident=df_identity)
             if cus_col:
-                cols=[ 'EEA', 'EPA', 'EAA', 'ModA', 'Actor', 'Behavior', 'ModO', 'Object']
+                columnss=[ 'EEA', 'EPA', 'EAA', 'ModA', 'Actor', 'Behavior', 'ModO', 'Object']
             
 
         if wt=='behavior': 
             df_behavior=pd.concat([Behaviors,new_df],axis=0)
             df2=out_df(data=q,predictions=preds,df_beh=df_behavior)
             if cus_col:
-                cols=['EEB', 'EPB', 'EAB',  'ModA', 'Actor', 'Behavior', 'ModO', 'Object']            
+                columnss=['EEB', 'EPB', 'EAB',  'ModA', 'Actor', 'Behavior', 'ModO', 'Object']            
         if wt=='modifier': 
             df_modifier=pd.concat([Modifiers,new_df],axis=0)
             df2=out_df(data=q,predictions=preds,df_mod=df_modifier)
             if cus_col:
-                cols=['EEMA', 'EPMA', 'EAMA',  'ModA', 'Actor', 'Behavior', 'ModO', 'Object']
+                columnss=['EEMA', 'EPMA', 'EAMA',  'ModA', 'Actor', 'Behavior', 'ModO', 'Object']
         df=pd.concat([df,df2],axis=0)
     return(df[columnss])