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[SKL] Propagate parameters to booster during set_param. (#6416)

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Jiaming Yuan 2020-11-20 20:37:35 +08:00 committed by GitHub
parent cc581b3b6b
commit 2ce2a1a4d8
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2 changed files with 21 additions and 1 deletions
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4
python-package/xgboost/sklearn.py
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@ -334,6 +334,10 @@ class XGBModel(XGBModelBase):
else: else:
self.kwargs[key] = value self.kwargs[key] = value
if hasattr(self, '_Booster'):
parameters = self.get_xgb_params()
self.get_booster().set_param(parameters)
return self return self
def get_params(self, deep=True): def get_params(self, deep=True):

18
tests/python/test_with_sklearn.py
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@ -542,13 +542,29 @@ def test_sklearn_n_jobs():
assert clf.get_xgb_params()['n_jobs'] == 2 assert clf.get_xgb_params()['n_jobs'] == 2
def test_kwargs(): def test_parameters_access():
from sklearn import datasets
params = {'updater': 'grow_gpu_hist', 'subsample': .5, 'n_jobs': -1} params = {'updater': 'grow_gpu_hist', 'subsample': .5, 'n_jobs': -1}
clf = xgb.XGBClassifier(n_estimators=1000, **params) clf = xgb.XGBClassifier(n_estimators=1000, **params)
assert clf.get_params()['updater'] == 'grow_gpu_hist' assert clf.get_params()['updater'] == 'grow_gpu_hist'
assert clf.get_params()['subsample'] == .5 assert clf.get_params()['subsample'] == .5
assert clf.get_params()['n_estimators'] == 1000 assert clf.get_params()['n_estimators'] == 1000
clf = xgb.XGBClassifier(n_estimators=1, nthread=4)
X, y = datasets.load_iris(return_X_y=True)
clf.fit(X, y)
config = json.loads(clf.get_booster().save_config())
assert int(config['learner']['generic_param']['nthread']) == 4
clf.set_params(nthread=16)
config = json.loads(clf.get_booster().save_config())
assert int(config['learner']['generic_param']['nthread']) == 16
clf.predict(X)
config = json.loads(clf.get_booster().save_config())
assert int(config['learner']['generic_param']['nthread']) == 16
def test_kwargs_error(): def test_kwargs_error():
params = {'updater': 'grow_gpu_hist', 'subsample': .5, 'n_jobs': -1} params = {'updater': 'grow_gpu_hist', 'subsample': .5, 'n_jobs': -1}