Explicitly use sets in `ahisto_named_entity_search.recognition.evaluator`

ahisto_named_entity_search/recognition/evaluator.py

-from typing import Dict, Optional, Set, Iterable, List
+from typing import Dict, Optional, Set, List
 
 from more_itertools import zip_equal
 import torch
@@ -11,7 +11,7 @@ from adaptor.utils import AdaptationDataset
 CategoryName = str
 GroupName = str
 Category = int
-Group = Iterable[CategoryName]
+Group = Set[CategoryName]
 CategoryMap = Dict[CategoryName, Category]
 GroupMap = Dict[GroupName, Group]
 
@@ -20,9 +20,9 @@ FScore = float
 
 class AggregateMeanFScore(TokenClassificationEvaluator):
     GROUPS: GroupMap = {
-        'PER': ('B-PER', 'I-PER', 'B-ORG', 'I-ORG'),
-        'LOC': ('B-LOC', 'I-LOC', 'B-ORG', 'I-ORG'),
-        'O': ('O', 'B-MISC', 'I-MISC'),
+        'PER': {'B-PER', 'I-PER', 'B-ORG', 'I-ORG'},
+        'LOC': {'B-LOC', 'I-LOC', 'B-ORG', 'I-ORG'},
+        'O': {'O', 'B-MISC', 'I-MISC'},
     }
 
     def __init__(self, category_map: CategoryMap, group_name: Optional[GroupName],
@@ -66,9 +66,9 @@ class AggregateMeanFScore(TokenClassificationEvaluator):
         return f_score
 
     @classmethod
-    def get_all_group_names(cls) -> Iterable[GroupName]:
-        for group_name in cls.GROUPS.keys():
-            yield group_name
+    def get_all_group_names(cls) -> Set[GroupName]:
+        all_group_names = set(cls.GROUPS.keys())
+        return all_group_names
 
     def __str__(self) -> str:
         if self.group_name is None: