[python-package] Accept numpy generators as `random_state` (#6174)

python-package/lightgbm/compat.py

@@ -36,6 +36,16 @@ except ImportError:
 
     concat = None
 
+"""numpy"""
+try:
+    from numpy.random import Generator as np_random_Generator
+except ImportError:
+    class np_random_Generator:  # type: ignore
+        """Dummy class for np.random.Generator."""
+
+        def __init__(self, *args, **kwargs):
+            pass
+
 """matplotlib"""
 try:
     import matplotlib  # noqa: F401

python-package/lightgbm/dask.py

@@ -1142,7 +1142,7 @@ class DaskLGBMClassifier(LGBMClassifier, _DaskLGBMModel):
         colsample_bytree: float = 1.,
         reg_alpha: float = 0.,
         reg_lambda: float = 0.,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        random_state: Optional[Union[int, np.random.RandomState, 'np.random.Generator']] = None,
         n_jobs: Optional[int] = None,
         importance_type: str = 'split',
         client: Optional[Client] = None,
@@ -1347,7 +1347,7 @@ class DaskLGBMRegressor(LGBMRegressor, _DaskLGBMModel):
         colsample_bytree: float = 1.,
         reg_alpha: float = 0.,
         reg_lambda: float = 0.,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        random_state: Optional[Union[int, np.random.RandomState, 'np.random.Generator']] = None,
         n_jobs: Optional[int] = None,
         importance_type: str = 'split',
         client: Optional[Client] = None,
@@ -1517,7 +1517,7 @@ class DaskLGBMRanker(LGBMRanker, _DaskLGBMModel):
         colsample_bytree: float = 1.,
         reg_alpha: float = 0.,
         reg_lambda: float = 0.,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        random_state: Optional[Union[int, np.random.RandomState, 'np.random.Generator']] = None,
         n_jobs: Optional[int] = None,
         importance_type: str = 'split',
         client: Optional[Client] = None,

python-package/lightgbm/sklearn.py

@@ -15,7 +15,7 @@ from .callback import _EvalResultDict, record_evaluation
 from .compat import (SKLEARN_INSTALLED, LGBMNotFittedError, _LGBMAssertAllFinite, _LGBMCheckArray,
                      _LGBMCheckClassificationTargets, _LGBMCheckSampleWeight, _LGBMCheckXY, _LGBMClassifierBase,
                      _LGBMComputeSampleWeight, _LGBMCpuCount, _LGBMLabelEncoder, _LGBMModelBase, _LGBMRegressorBase,
-                     dt_DataTable, pd_DataFrame)
+                     dt_DataTable, np_random_Generator, pd_DataFrame)
 from .engine import train
 
 __all__ = [
@@ -448,7 +448,7 @@ class LGBMModel(_LGBMModelBase):
         colsample_bytree: float = 1.,
         reg_alpha: float = 0.,
         reg_lambda: float = 0.,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        random_state: Optional[Union[int, np.random.RandomState, 'np.random.Generator']] = None,
         n_jobs: Optional[int] = None,
         importance_type: str = 'split',
         **kwargs
@@ -509,7 +509,7 @@ class LGBMModel(_LGBMModelBase):
         random_state : int, RandomState object or None, optional (default=None)
             Random number seed.
             If int, this number is used to seed the C++ code.
-            If RandomState object (numpy), a random integer is picked based on its state to seed the C++ code.
+            If RandomState or Generator object (numpy), a random integer is picked based on its state to seed the C++ code.
             If None, default seeds in C++ code are used.
         n_jobs : int or None, optional (default=None)
             Number of parallel threads to use for training (can be changed at prediction time by
@@ -710,6 +710,10 @@ class LGBMModel(_LGBMModelBase):
 
         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)
+            )
         if self._n_classes > 2:
             for alias in _ConfigAliases.get('num_class'):
                 params.pop(alias, None)

tests/python_package_test/test_sklearn.py

@@ -534,11 +534,12 @@ def test_non_serializable_objects_in_callbacks(tmp_path):
     assert gbm.booster_.attr_set_inside_callback == 40
 
 
-def test_random_state_object():
+@pytest.mark.parametrize("rng_constructor", [np.random.RandomState, np.random.default_rng])
+def test_random_state_object(rng_constructor):
     X, y = load_iris(return_X_y=True)
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
-    state1 = np.random.RandomState(123)
-    state2 = np.random.RandomState(123)
+    state1 = rng_constructor(123)
+    state2 = rng_constructor(123)
     clf1 = lgb.LGBMClassifier(n_estimators=10, subsample=0.5, subsample_freq=1, random_state=state1)
     clf2 = lgb.LGBMClassifier(n_estimators=10, subsample=0.5, subsample_freq=1, random_state=state2)
     # Test if random_state is properly stored