diff --git a/outrank/core_ranking.py b/outrank/core_ranking.py
index 6d1e855..a406c26 100644
--- a/outrank/core_ranking.py
+++ b/outrank/core_ranking.py
@@ -116,12 +116,21 @@ def mixed_rank_graph(
     out_time_struct['encoding_columns'] = end_enc_timer - start_enc_timer
 
     combinations = get_combinations_from_columns(all_columns, args)
-    #combinations = prior_combinations_sample(combinations, args)
-    #random.shuffle(combinations)
 
     reference_model_features = {}
     if is_prior_heuristic(args):
-        reference_model_features = [(" AND ").join(tuple(sorted(item.split(",")))) for item in extract_features_from_reference_JSON(args.reference_model_JSON, full_feature_space = True)]
+        reference_model_features = [(" AND ").join(tuple(sorted(item.split(",")))) for item in extract_features_from_reference_JSON(args.reference_model_JSON, all_features=True)]
+        combinations = [comb for comb in combinations if comb[0] not in reference_model_features and comb[1] not in reference_model_features]
+        print(combinations)
+        print("\n\n")
+
+    combinations = prior_combinations_sample(combinations, args)
+    print(GLOBAL_PRIOR_COMB_COUNTS)
+    print(combinations)
+    print("\n\n")
+    random.shuffle(combinations)
+    print(combinations)
+    print("\n\n")
 
     if args.heuristic == 'Constant':
         final_constant_imp = []
@@ -196,30 +205,20 @@ def compute_combined_features(
 
     model_combinations = []
     full_combination_space = []
-    if is_prior_heuristic(args):
+
+    if args.reference_model_JSON != '':
         model_combinations = extract_features_from_reference_JSON(args.reference_model_JSON, combined_features_only = True)
         model_combinations = [tuple(sorted(combination.split(','))) for combination in model_combinations]
-        if args.interaction_order > 1:
-            full_combination_space = list(
-                itertools.combinations(all_columns, interaction_order),
-            )
-    else:
-        if args.reference_model_JSON != '':
-            model_combinations = extract_features_from_reference_JSON(args.reference_model_JSON, combined_features_only = True)
-            model_combinations = [tuple(sorted(combination.split(','))) for combination in model_combinations]
-            full_combination_space = model_combinations
-        else:
+        full_combination_space = model_combinations
+
+    if args.interaction_order > 1:
             full_combination_space = list(
                 itertools.combinations(all_columns, interaction_order),
             )
+    full_combination_space = prior_combinations_sample(full_combination_space, args)
 
-    if args.combination_number_upper_bound:
-        random.shuffle(full_combination_space)
-        full_combination_space = full_combination_space[
-            : args.combination_number_upper_bound
-        ]
-        if is_prior_heuristic(args):
-            full_combination_space = full_combination_space + [tuple for tuple in model_combinations if tuple not in full_combination_space]
+    if is_prior_heuristic(args):
+        full_combination_space = full_combination_space + [tuple for tuple in model_combinations if tuple not in full_combination_space]
 
 
     com_counter = 0