Update app.py

app.py

@@ -66,16 +66,23 @@ search_query = sorted(query2outputs)[0]
 def aggregate(list_of_hits):
     import numpy as np
     from permsc import KemenyOptimalAggregator, sum_kendall_tau, ranks_from_preferences
+    from permsc import BordaRankAggregator
 
     preferences = []
     for result in list_of_hits:
         preferences.append([doc["rank"] for doc in result])
 
     preferences = np.array(preferences)
-    y_optimal = KemenyOptimalAggregator().aggregate(preferences)
+    # y_optimal = KemenyOptimalAggregator().aggregate(preferences)
+    y_optimal = BordaRankAggregator().aggregate(preferences)
+
     rank2doc = {}
     for doc in list_of_hits[0]:
         rank2doc[doc["rank"]] = doc
+
+    print("preferences: ", preferences.shape, preferences[0])
+    print("rank2doc:", rank2doc.keys())
+    print("y_optimal: ", y_optimal)
     return [rank2doc[rank] for rank in y_optimal]
 
 aggregated_ranking = aggregate(query2outputs[search_query])