[python-package] mark EarlyStopException as part of public API (#6095)

python-package/lightgbm/__init__.py

@@ -6,7 +6,7 @@
 from pathlib import Path
 
 from .basic import Booster, Dataset, Sequence, register_logger
-from .callback import early_stopping, log_evaluation, record_evaluation, reset_parameter
+from .callback import EarlyStopException, early_stopping, log_evaluation, record_evaluation, reset_parameter
 from .engine import CVBooster, cv, train
 
 try:
@@ -32,5 +32,5 @@
            'train', 'cv',
            'LGBMModel', 'LGBMRegressor', 'LGBMClassifier', 'LGBMRanker',
            'DaskLGBMRegressor', 'DaskLGBMClassifier', 'DaskLGBMRanker',
-           'log_evaluation', 'record_evaluation', 'reset_parameter', 'early_stopping',
+           'log_evaluation', 'record_evaluation', 'reset_parameter', 'early_stopping', 'EarlyStopException',
            'plot_importance', 'plot_split_value_histogram', 'plot_metric', 'plot_tree', 'create_tree_digraph']

python-package/lightgbm/callback.py

@@ -12,6 +12,7 @@
     from .engine import CVBooster
 
 __all__ = [
+    'EarlyStopException',
     'early_stopping',
     'log_evaluation',
     'record_evaluation',
@@ -30,7 +31,11 @@
 
 
 class EarlyStopException(Exception):
-    """Exception of early stopping."""
+    """Exception of early stopping.
+
+    Raise this from a callback passed in via keyword argument ``callbacks``
+    in ``cv()`` or ``train()`` to trigger early stopping.
+    """
 
     def __init__(self, best_iteration: int, best_score: _ListOfEvalResultTuples) -> None:
         """Create early stopping exception.
@@ -39,6 +44,7 @@ def __init__(self, best_iteration: int, best_score: _ListOfEvalResultTuples) ->
         ----------
         best_iteration : int
             The best iteration stopped.
+            0-based... pass ``best_iteration=2`` to indicate that the third iteration was the best one.
         best_score : list of (eval_name, metric_name, eval_result, is_higher_better) tuple or (eval_name, metric_name, eval_result, is_higher_better, stdv) tuple
             Scores for each metric, on each validation set, as of the best iteration.
         """

tests/python_package_test/test_engine.py

@@ -1092,6 +1092,33 @@ def test_early_stopping_min_delta(first_only, single_metric, greater_is_better):
         assert np.greater_equal(last_score, best_score - min_delta).any()
 
 
+def test_early_stopping_can_be_triggered_via_custom_callback():
+    X, y = make_synthetic_regression()
+
+    def _early_stop_after_seventh_iteration(env):
+        if env.iteration == 6:
+            exc = lgb.EarlyStopException(
+                best_iteration=6,
+                best_score=[("some_validation_set", "some_metric", 0.708, True)]
+            )
+            raise exc
+
+    bst = lgb.train(
+        params={
+            "objective": "regression",
+            "verbose": -1,
+            "num_leaves": 2
+        },
+        train_set=lgb.Dataset(X, label=y),
+        num_boost_round=23,
+        callbacks=[_early_stop_after_seventh_iteration]
+    )
+    assert bst.num_trees() == 7
+    assert bst.best_score["some_validation_set"]["some_metric"] == 0.708
+    assert bst.best_iteration == 7
+    assert bst.current_iteration() == 7
+
+
 def test_continue_train():
     X, y = make_synthetic_regression()
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)