Fix typos in `ahisto_named_entity_search.recognition.model`

ahisto_named_entity_search/recognition/model.py

@@ -22,6 +22,9 @@ from .schedule import ScheduleName, get_schedule
 LOGGER = getLogger(__name__)
 
 
+EvaluationResult = float
+
+
 class NerModel:
     CONFIG = _CONFIG['recognition.NerModel']
     ROOT_PATH = Path(CONFIG['root_path'])
@@ -49,25 +52,28 @@ class NerModel:
     def __repr__(self) -> str:
         return '{}: {}'.format(self.__class__.__name__, self)
 
-    def test(self, test_tagged_sentence_basename: str) -> float:
+    def test(self, test_tagged_sentence_basename: str) -> EvaluationResult:
         lang_module = LangModule(self.model_name_or_basename)
 
         ner_test_texts, ner_test_labels = load_ner_dataset(test_tagged_sentence_basename)
-        ner_evaluators = [MeanFScore(evaluation_strategy='steps')]
+        ner_evaluator = MeanFScore()
         ner_objective = TokenClassification(lang_module,
                                             batch_size=1,
-                                            texts_or_path=ner_test_texts,
-                                            labels_or_path=ner_test_labels,
-                                            val_texts_or_path=ner_testn_texts,
-                                            val_labels_or_path=ner_testn_labels,
-                                            val_evaluators=ner_evaluators)
-
-        adaptation_arguments = AdaptationArguments()
+                                            texts_or_path=[],
+                                            labels_or_path=[],
+                                            val_texts_or_path=ner_test_texts,
+                                            val_labels_or_path=ner_test_labels,
+                                            val_evaluators=[ner_evaluator])
+        adaptation_arguments = AdaptationArguments(
+            output_dir='.',
+            stopping_strategy=StoppingStrategy.FIRST_OBJECTIVE_CONVERGED,
+            evaluation_strategy='steps',
+        )
         schedule = get_schedule('sequential', [ner_objective], adaptation_arguments)
         adapter = Adapter(lang_module, schedule, adaptation_arguments)
 
-        test_result = adapter.evaluate()
-        test_f_score = test_result['eval_MeanFScore']
+        test_result = ner_objective.per_objective_log("eval")
+        test_f_score = test_result[f'eval_{ner_objective}_{ner_evaluator}']
         return test_f_score
 
     @classmethod
@@ -139,7 +145,7 @@ class NerModel:
         return cls.load(model_basename)
 
     @classmethod
-    def load(cls, basename: str) -> 'NerModel':
+    def load(cls, model_basename: str) -> 'NerModel':
         model_pathname = cls.ROOT_PATH / model_basename
         model_pathname = model_pathname / 'TokenClassification'
         model_name_or_basename = str(model_pathname)