Remove n_iter_no_change parameter

python-package/lightgbm/dask.py

@@ -1137,7 +1137,6 @@ def __init__(
         client: Optional[Client] = None,
         *,
         early_stopping: bool = False,
-        n_iter_no_change: int = 10,
         validation_fraction: Optional[float] = 0.1,
         **kwargs: Any,
     ):
@@ -1344,7 +1343,6 @@ def __init__(
         client: Optional[Client] = None,
         *,
         early_stopping: bool = False,
-        n_iter_no_change: int = 10,
         validation_fraction: Optional[float] = 0.1,
         **kwargs: Any,
     ):
@@ -1515,7 +1513,6 @@ def __init__(
         client: Optional[Client] = None,
         *,
         early_stopping: bool = False,
-        n_iter_no_change: int = 10,
         validation_fraction: Optional[float] = 0.1,
         **kwargs: Any,
     ):

python-package/lightgbm/sklearn.py

@@ -508,8 +508,7 @@ def __init__(
         n_jobs: Optional[int] = None,
         importance_type: str = "split",
         *,
-        early_stopping: bool = False,
-        n_iter_no_change: int = 10,
+        early_stopping: Union[bool, int] = False,
         validation_fraction: Optional[float] = 0.1,
         **kwargs: Any,
     ):
@@ -591,12 +590,15 @@ def __init__(
             The type of feature importance to be filled into ``feature_importances_``.
             If 'split', result contains numbers of times the feature is used in a model.
             If 'gain', result contains total gains of splits which use the feature.
-        early_stopping : bool, optional (default=False)
-            Whether to enable early stopping. If set to True, training will stop if the validation score does not improve
-            for a specified number of rounds (controlled by `n_iter_no_change`).
-        n_iter_no_change : int, optional (default=10)
-            If early stopping is enabled, this parameter specifies the number of iterations with no
-            improvement after which training will be stopped.
+        early_stopping : bool, optional (default=False) Whether to enable scikit-learn-style early
+            stopping. If set to ``True` and no other validation set is passed to ``fit()``, a new
+            validation set will be created by randomly sampling ``validation_fraction`` rows from
+            the training data ``X`` passed to ``fit()``. Training will stop if the validation score
+            does not improve for a specific number of rounds (controlled by ``n_iter_no_change``).
+            This parameter is here for compatibility with ``scikit-learn``'s
+            ``HistGradientBoosting`` estimators. it does not affect other ``lightgbm``-specific
+            early stopping mechanisms, like passing the ``lgb.early_stopping`` callback and
+            validation sets to the ``eval_set`` argument of `fit()`.
         validation_fraction : float or None, optional (default=0.1)
             Proportion of training data to set aside as
             validation data for early stopping. If None, early stopping is done on
@@ -666,7 +668,6 @@ def __init__(
         self.n_jobs = n_jobs
         self.importance_type = importance_type
         self.early_stopping = early_stopping
-        self.n_iter_no_change = n_iter_no_change
         self.validation_fraction = validation_fraction
         self._Booster: Optional[Booster] = None
         self._evals_result: _EvalResultDict = {}
@@ -834,18 +835,12 @@ def _process_params(self, stage: str) -> Dict[str, Any]:
         params.pop("n_estimators", None)
         params.pop("class_weight", None)
         params.pop("validation_fraction", None)
-        params.pop("early_stopping", None)
-        params.pop("n_iter_no_change", None)
 
         if isinstance(params["random_state"], np.random.RandomState):
             params["random_state"] = params["random_state"].randint(np.iinfo(np.int32).max)
         elif isinstance(params["random_state"], np.random.Generator):
             params["random_state"] = int(params["random_state"].integers(np.iinfo(np.int32).max))
 
-        params = _choose_param_value("early_stopping_round", params, self.n_iter_no_change)
-        if self.early_stopping is not True:
-            params["early_stopping_round"] = None
-
         if self._n_classes > 2:
             for alias in _ConfigAliases.get("num_class"):
                 params.pop(alias, None)
@@ -878,7 +873,28 @@ def _process_params(self, stage: str) -> Dict[str, Any]:
             params = _choose_param_value("num_threads", params, self.n_jobs)
             params["num_threads"] = self._process_n_jobs(params["num_threads"])
 
-        return params
+        if not isinstance(self.early_stopping, bool) and isinstance(self.early_stopping, int):
+            _log_warning(
+                f"Found 'early_stopping={self.early_stopping}' passed through keyword arguments. "
+                "Future versions of 'lightgbm' will not allow this, as scikit-learn expects keyword argument "
+                "'early_stopping' to be a boolean indicating whether or not to perform early stopping with "
+                "a randomly-sampled validation set. To set the number of early stopping rounds, and suppress "
+                f"this warning, pass early_stopping_rounds={self.early_stopping} instead."
+            )
+            params = _choose_param_value(
+                main_param_name="early_stopping_round", params=params, default_value=self.early_stopping
+            )
+
+        params.pop("early_stopping", None)
+
+        if isinstance(self.early_stopping, bool) and self.early_stopping is True:
+            default_early_stopping_round = 10
+        else:
+            default_early_stopping_round = None
+
+        return _choose_param_value(
+            main_param_name="early_stopping_round", params=params, default_value=default_early_stopping_round
+        )
 
     def _process_n_jobs(self, n_jobs: Optional[int]) -> int:
         """Convert special values of n_jobs to their actual values according to the formulas that apply.

tests/python_package_test/test_sklearn.py

@@ -1274,7 +1274,6 @@ def fit_and_check(eval_set_names, metric_names, assumed_iteration, first_metric_
         "verbose": -1,
         "seed": 123,
         "early_stopping_rounds": 5,
-        "early_stopping": True,
     }  # early stop should be supported via global LightGBM parameter
     params_fit = {"X": X_train, "y": y_train}