fix: execute single query

valid_efficiency_score.py

@@ -42,8 +42,8 @@ Args:
         should be a string with tokens separated by spaces.
     references: list of reference for each prediction. Each
         reference should be a string with tokens separated by spaces.
-    execute: function that takes a list of sql queries and returns a list of results, one for each query. 
-        Results should be a list of tuples, each tuple containing the values of a row.
+    execute: function that takes a sql query and returns a result. 
+        The result should be a list of tuples, each tuple containing the values of a row.
     filter_func: function that takes a string and returns a boolean.
         If True, the string is kept, otherwise it is dropped.
     num_executions: number of times to execute each sql query to get the execution time.
@@ -107,21 +107,19 @@ class ValidEfficiencyScore(evaluate.Metric):
                 elif filter_func(reference):
                     passing_reference_only += 1
 
-        # Execute ground truth sql queries to get the ground truth results and the time it takes to execute them
-        ground_results = execute(filtered_references)
+        # Execute ground truth sql queries to get the time it takes to execute them
         reference_times = np.zeros(num_executions)
         for i in range(num_executions):
             start_time = time()
-            execute(filtered_references)
+            [execute(i) for i in filtered_references]
             end_time = time()
             reference_times[i] = end_time - start_time
 
-        # Execute predicted sql queries to get the predicted results and the time it takes to execute them
-        predicted_results = execute(filtered_predictions)
+        # Execute predicted sql queries to get the time it takes to execute them
         prediction_times = np.zeros(num_executions)
         for i in range(num_executions):
             start_time = time()
-            execute(filtered_predictions)
+            [execute(i) for i in filtered_predictions]
             end_time = time()
             prediction_times[i] = end_time - start_time