[python] add more type hints on LGBMModel methods (#5239)

python-package/lightgbm/sklearn.py

@@ -505,7 +505,7 @@ def __init__(
         self._n_classes = None
         self.set_params(**kwargs)
 
-    def _more_tags(self):
+    def _more_tags(self) -> Dict[str, Any]:
         return {
             'allow_nan': True,
             'X_types': ['2darray', 'sparse', '1dlabels'],
@@ -520,7 +520,7 @@ def _more_tags(self):
     def __sklearn_is_fitted__(self) -> bool:
         return getattr(self, "fitted_", False)
 
-    def get_params(self, deep=True):
+    def get_params(self, deep: bool = True) -> Dict[str, Any]:
         """Get parameters for this estimator.
 
         Parameters
@@ -538,7 +538,7 @@ def get_params(self, deep=True):
         params.update(self._other_params)
         return params
 
-    def set_params(self, **params):
+    def set_params(self, **params: Any) -> "LGBMModel":
         """Set the parameters of this estimator.
 
         Parameters
@@ -823,14 +823,14 @@ def predict(self, X, raw_score=False, start_iteration=0, num_iteration=None,
     )
 
     @property
-    def n_features_(self):
+    def n_features_(self) -> int:
         """:obj:`int`: The number of features of fitted model."""
         if not self.__sklearn_is_fitted__():
             raise LGBMNotFittedError('No n_features found. Need to call fit beforehand.')
         return self._n_features
 
     @property
-    def n_features_in_(self):
+    def n_features_in_(self) -> int:
         """:obj:`int`: The number of features of fitted model."""
         if not self.__sklearn_is_fitted__():
             raise LGBMNotFittedError('No n_features_in found. Need to call fit beforehand.')
@@ -844,14 +844,14 @@ def best_score_(self):
         return self._best_score
 
     @property
-    def best_iteration_(self):
+    def best_iteration_(self) -> int:
         """:obj:`int`: The best iteration of fitted model if ``early_stopping()`` callback has been specified."""
         if not self.__sklearn_is_fitted__():
             raise LGBMNotFittedError('No best_iteration found. Need to call fit with early_stopping callback beforehand.')
         return self._best_iteration
 
     @property
-    def objective_(self):
+    def objective_(self) -> Union[str, _LGBM_ScikitCustomObjectiveFunction]:
         """:obj:`str` or :obj:`callable`: The concrete objective used while fitting this model."""
         if not self.__sklearn_is_fitted__():
             raise LGBMNotFittedError('No objective found. Need to call fit beforehand.')
@@ -1088,7 +1088,7 @@ def classes_(self):
         return self._classes
 
     @property
-    def n_classes_(self):
+    def n_classes_(self) -> int:
         """:obj:`int`: The number of classes."""
         if not self.__sklearn_is_fitted__():
             raise LGBMNotFittedError('No classes found. Need to call fit beforehand.')