[python-package] fix mypy errors about _InnerPredictor (#5714)

python-package/lightgbm/basic.py

@@ -1375,9 +1375,9 @@ class Dataset:
         self.categorical_feature = categorical_feature
         self.params = deepcopy(params)
         self.free_raw_data = free_raw_data
-        self.used_indices = None
+        self.used_indices: Optional[List[int]] = None
         self.need_slice = True
-        self._predictor = None
+        self._predictor: Optional[_InnerPredictor] = None
         self.pandas_categorical = None
         self.params_back_up = None
         self.monotone_constraints = None
@@ -1583,7 +1583,12 @@ class Dataset:
             self.data = None
         return self
 
-    def _set_init_score_by_predictor(self, predictor, data, used_indices=None):
+    def _set_init_score_by_predictor(
+        self,
+        predictor: Optional[_InnerPredictor],
+        data,
+        used_indices: Optional[List[int]]
+    ):
         data_has_header = False
         if isinstance(data, (str, Path)):
             # check data has header or not
@@ -1721,7 +1726,11 @@ class Dataset:
         if isinstance(predictor, _InnerPredictor):
             if self._predictor is None and init_score is not None:
                 _log_warning("The init_score will be overridden by the prediction of init_model.")
-            self._set_init_score_by_predictor(predictor, data)
+            self._set_init_score_by_predictor(
+                predictor=predictor,
+                data=data,
+                used_indices=None
+            )
         elif init_score is not None:
             self.set_init_score(init_score)
         elif predictor is not None:
@@ -2034,7 +2043,11 @@ class Dataset:
                         raise ValueError("Label should not be None.")
                     if isinstance(self._predictor, _InnerPredictor) and self._predictor is not self.reference._predictor:
                         self.get_data()
-                        self._set_init_score_by_predictor(self._predictor, self.data, used_indices)
+                        self._set_init_score_by_predictor(
+                            predictor=self._predictor,
+                            data=self.data,
+                            used_indices=used_indices
+                        )
             else:
                 # create train
                 self._lazy_init(self.data, label=self.label,
@@ -2323,16 +2336,27 @@ class Dataset:
         It is not recommended for user to call this function.
         Please use init_model argument in engine.train() or engine.cv() instead.
         """
-        if predictor is self._predictor and (predictor is None or predictor.current_iteration() == self._predictor.current_iteration()):
+        if predictor is None and self._predictor is None:
             return self
+        elif isinstance(predictor, _InnerPredictor) and isinstance(self._predictor, _InnerPredictor):
+            if (predictor == self._predictor) and (predictor.current_iteration() == self._predictor.current_iteration()):
+                return self
         if self.handle is None:
             self._predictor = predictor
         elif self.data is not None:
             self._predictor = predictor
-            self._set_init_score_by_predictor(self._predictor, self.data)
+            self._set_init_score_by_predictor(
+                predictor=self._predictor,
+                data=self.data,
+                used_indices=None
+            )
         elif self.used_indices is not None and self.reference is not None and self.reference.data is not None:
             self._predictor = predictor
-            self._set_init_score_by_predictor(self._predictor, self.reference.data, self.used_indices)
+            self._set_init_score_by_predictor(
+                predictor=self._predictor,
+                data=self.reference.data,
+                used_indices=self.used_indices
+            )
         else:
             raise LightGBMError("Cannot set predictor after freed raw data, "
                                 "set free_raw_data=False when construct Dataset to avoid this.")