CU-8698jzjj3: fix mypy errors

Author: Tom Searle

medcat-v1/medcat/utils/ner/deid.py

@@ -64,14 +64,10 @@ def __init__(self, cat: CAT) -> None:
         self.cat = cat
 
     def train(self, json_path: Union[str, list, None]=None, 
-              train_json_path: Union[str, list, None]=None, 
-              test_json_path: Union[str, list, None]=None, 
               *args, **kwargs) -> Tuple[Any, Any, Any]:
-        assert not all([json_path, train_json_path, test_json_path]), \
+        assert not all([json_path, kwargs.get('train_json_path'), kwargs.get('test_json_path')]), \
                 "Either json_path or train_json_path and test_json_path must be provided when no dataset is provided"
-        return super().train(json_path=json_path, 
-                             train_json_path=train_json_path, 
-                             test_json_path=test_json_path, *args, **kwargs)  # type: ignore
+        return super().train(json_path=json_path, *args, **kwargs)  # type: ignore
 
     def eval(self, json_path: Union[str, list, None],
               *args, **kwargs) -> Tuple[Any, Any, Any]:
@@ -237,7 +233,7 @@ def match_rules(rules: List[Tuple[str, str]], texts: List[str], cat: CAT):
     return rule_matches_per_text
 
 
-def merge_preds(model_preds_by_text: List[Dict], rule_matches_per_text: List[Dict], accept_preds=True):
+def merge_preds(model_preds_by_text: List[List[Dict]], rule_matches_per_text: List[List[Dict]], accept_preds=True):
     """
     Merge predictions from rule based and deID model predictions for further evaluation
 

medcat-v1/medcat/utils/ner/model.py

@@ -26,8 +26,6 @@ def __init__(self, cat: CAT) -> None:
         self.cat = cat
 
     def train(self, json_path: Union[str, list, None], train_nr: int = 0,
-              train_json_path: Union[str, list, None]=None,
-              test_json_path: Union[str, list, None]=None,
               *args, **kwargs) -> Tuple[Any, Any, Any]:
         """Train the underlying transformers NER model.
 
@@ -42,7 +40,7 @@ def train(self, json_path: Union[str, list, None], train_nr: int = 0,
         Returns:
             Tuple[Any, Any, Any]: df, examples, dataset
         """
-        return self.cat._addl_ner[train_nr].train(json_path, train_json_path=train_json_path, test_json_path=test_json_path, *args, **kwargs)
+        return self.cat._addl_ner[train_nr].train(json_path, *args, **kwargs)
 
     def eval(self, json_path: Union[str, list, None], train_nr: int = 0,
               *args, **kwargs) -> Tuple[Any, Any, Any]: