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[Python] Accept numpy generators as random_state (#9743)

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* accept numpy generators for random_state

* make linter happy

* fix tests
This commit is contained in:
david-cortes 2023-11-02 00:20:44 +01:00 committed by GitHub
parent 4da4e092b5
commit be20df8c23
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2 changed files with 12 additions and 2 deletions
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10
python-package/xgboost/sklearn.py
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@ -248,7 +248,7 @@ __model_doc = f"""
Balancing of positive and negative weights. Balancing of positive and negative weights.
base_score : Optional[float] base_score : Optional[float]
The initial prediction score of all instances, global bias. The initial prediction score of all instances, global bias.
random_state : Optional[Union[numpy.random.RandomState, int]] random_state : Optional[Union[numpy.random.RandomState, numpy.random.Generator, int]]
Random number seed. Random number seed.
.. note:: .. note::
@ -651,7 +651,9 @@ class XGBModel(XGBModelBase):
reg_lambda: Optional[float] = None, reg_lambda: Optional[float] = None,
scale_pos_weight: Optional[float] = None, scale_pos_weight: Optional[float] = None,
base_score: Optional[float] = None, base_score: Optional[float] = None,
random_state: Optional[Union[np.random.RandomState, int]] = None, random_state: Optional[
Union[np.random.RandomState, np.random.Generator, int]
] = None,
missing: float = np.nan, missing: float = np.nan,
num_parallel_tree: Optional[int] = None, num_parallel_tree: Optional[int] = None,
monotone_constraints: Optional[Union[Dict[str, int], str]] = None, monotone_constraints: Optional[Union[Dict[str, int], str]] = None,
@ -789,6 +791,10 @@ class XGBModel(XGBModelBase):
params["random_state"] = params["random_state"].randint( params["random_state"] = params["random_state"].randint(
np.iinfo(np.int32).max 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)
)
return params return params

4
tests/python/test_with_sklearn.py
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@ -702,6 +702,10 @@ def test_sklearn_random_state():
clf = xgb.XGBClassifier(random_state=random_state) clf = xgb.XGBClassifier(random_state=random_state)
assert isinstance(clf.get_xgb_params()['random_state'], int) assert isinstance(clf.get_xgb_params()['random_state'], int)
random_state = np.random.default_rng(seed=404)
clf = xgb.XGBClassifier(random_state=random_state)
assert isinstance(clf.get_xgb_params()['random_state'], int)
def test_sklearn_n_jobs(): def test_sklearn_n_jobs():
clf = xgb.XGBClassifier(n_jobs=1) clf = xgb.XGBClassifier(n_jobs=1)