[python-package] add type hints on predict() methods (#5334)

* [python-package] add type hints on predict() methods

* formatting

python-package/lightgbm/basic.py

@@ -756,8 +756,17 @@ class _InnerPredictor:
         this.pop('handle', None)
         return this
 
-    def predict(self, data, start_iteration=0, num_iteration=-1,
-                raw_score=False, pred_leaf=False, pred_contrib=False, data_has_header=False, validate_features=False):
+    def predict(
+        self,
+        data,
+        start_iteration: int = 0,
+        num_iteration: int = -1,
+        raw_score: bool = False,
+        pred_leaf: bool = False,
+        pred_contrib: bool = False,
+        data_has_header: bool = False,
+        validate_features: bool = False
+    ):
         """Predict logic.
 
         Parameters
@@ -3513,10 +3522,18 @@ class Booster:
                                                           default=json_default_with_numpy))
         return ret
 
-    def predict(self, data, start_iteration=0, num_iteration=None,
-                raw_score=False, pred_leaf=False, pred_contrib=False,
-                data_has_header=False, validate_features=False,
-                **kwargs):
+    def predict(
+        self,
+        data,
+        start_iteration: int = 0,
+        num_iteration: Optional[int] = None,
+        raw_score: bool = False,
+        pred_leaf: bool = False,
+        pred_contrib: bool = False,
+        data_has_header: bool = False,
+        validate_features: bool = False,
+        **kwargs: Any
+    ):
         """Make a prediction.
 
         Parameters

python-package/lightgbm/sklearn.py

@@ -826,8 +826,17 @@ class LGBMModel(_LGBMModelBase):
         eval_group_shape="list of array, or None, optional (default=None)"
     ) + "\n\n" + _lgbmmodel_doc_custom_eval_note
 
-    def predict(self, X, raw_score=False, start_iteration=0, num_iteration=None,
-                pred_leaf=False, pred_contrib=False, validate_features=False, **kwargs):
+    def predict(
+        self,
+        X,
+        raw_score: bool = False,
+        start_iteration: int = 0,
+        num_iteration: Optional[int] = None,
+        pred_leaf: bool = False,
+        pred_contrib: bool = False,
+        validate_features: bool = False,
+        **kwargs: Any
+    ):
         """Docstring is set after definition, using a template."""
         if not self.__sklearn_is_fitted__():
             raise LGBMNotFittedError("Estimator not fitted, call fit before exploiting the model.")
@@ -1094,9 +1103,17 @@ class LGBMClassifier(_LGBMClassifierBase, LGBMModel):
     fit.__doc__ = (_base_doc[:_base_doc.find('eval_group :')]
                    + _base_doc[_base_doc.find('eval_metric :'):])
 
-    def predict(self, X, raw_score=False, start_iteration=0, num_iteration=None,
-                pred_leaf=False, pred_contrib=False, validate_features=False,
-                **kwargs):
+    def predict(
+        self,
+        X,
+        raw_score: bool = False,
+        start_iteration: int = 0,
+        num_iteration: Optional[int] = None,
+        pred_leaf: bool = False,
+        pred_contrib: bool = False,
+        validate_features: bool = False,
+        **kwargs: Any
+    ):
         """Docstring is inherited from the LGBMModel."""
         result = self.predict_proba(X, raw_score, start_iteration, num_iteration,
                                     pred_leaf, pred_contrib, validate_features,
@@ -1109,8 +1126,17 @@ class LGBMClassifier(_LGBMClassifierBase, LGBMModel):
 
     predict.__doc__ = LGBMModel.predict.__doc__
 
-    def predict_proba(self, X, raw_score=False, start_iteration=0, num_iteration=None,
-                      pred_leaf=False, pred_contrib=False, validate_features=False, **kwargs):
+    def predict_proba(
+        self,
+        X,
+        raw_score: bool = False,
+        start_iteration: int = 0,
+        num_iteration: Optional[int] = None,
+        pred_leaf: bool = False,
+        pred_contrib: bool = False,
+        validate_features: bool = False,
+        **kwargs: Any
+    ):
         """Docstring is set after definition, using a template."""
         result = super().predict(X, raw_score, start_iteration, num_iteration, pred_leaf, pred_contrib, validate_features, **kwargs)
         if callable(self._objective) and not (raw_score or pred_leaf or pred_contrib):