[python-package] reorganize early stopping callback

python-package/lightgbm/callback.py

@@ -273,8 +273,8 @@ def __init__(
         min_delta: Union[float, List[float]] = 0.0
     ) -> None:
 
-        if stopping_rounds <= 0:
-            raise ValueError(f"stopping_rounds should be greater than zero. got: {stopping_rounds}")
+        if not isinstance(stopping_rounds, int) or stopping_rounds <= 0:
+            raise ValueError(f"stopping_rounds should be an integer and greater than 0. got: {stopping_rounds}")
 
         self.order = 30
         self.before_iteration = False

tests/python_package_test/test_callback.py

@@ -21,6 +21,17 @@ def test_early_stopping_callback_is_picklable(serializer):
     assert callback.stopping_rounds == rounds
 
 
+def test_early_stopping_callback_rejects_invalid_stopping_rounds_with_informative_errors():
+    with pytest.raises(ValueError, match="stopping_rounds should be an integer and greater than 0. got: 0"):
+        lgb.early_stopping(stopping_rounds=0)
+
+    with pytest.raises(ValueError, match="stopping_rounds should be an integer and greater than 0. got: -1"):
+        lgb.early_stopping(stopping_rounds=-1)
+
+    with pytest.raises(ValueError, match="stopping_rounds should be an integer and greater than 0. got: neverrrr"):
+        lgb.early_stopping(stopping_rounds="neverrrr")
+
+
 @pytest.mark.parametrize('serializer', SERIALIZERS)
 def test_log_evaluation_callback_is_picklable(serializer):
     periods = 42

tests/python_package_test/test_engine.py

@@ -4501,9 +4501,9 @@ def test_train_raises_informative_error_if_any_valid_sets_are_not_dataset_object
 
 def test_train_raises_informative_error_for_params_of_wrong_type():
     X, y = make_synthetic_regression()
-    params = {"early_stopping_round": "too-many"}
+    params = {"num_leaves": "too-many"}
     dtrain = lgb.Dataset(X, label=y)
-    with pytest.raises(lgb.basic.LightGBMError, match="Parameter early_stopping_round should be of type int, got \"too-many\""):
+    with pytest.raises(lgb.basic.LightGBMError, match="Parameter num_leaves should be of type int, got \"too-many\""):
         lgb.train(params, dtrain)