Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Allow using RandomState object from Numpy in sklearn interface. (#5049)

Browse Source
This commit is contained in:
Jiaming Yuan 2019-11-19 10:56:39 +08:00 committed by GitHub
parent 4d2779663e
commit a4f5c86276
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 7 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
python-package/xgboost/sklearn.py
View File

@ -230,6 +230,9 @@ class XGBModel(XGBModelBase):
params['missing'] = None # sklearn doesn't handle nan. see #4725
if not params.get('eval_metric', True):
del params['eval_metric'] # don't give as None param to Booster
if isinstance(params['random_state'], np.random.RandomState):
params['random_state'] = params['random_state'].randint(
np.iinfo(np.int32).max)
return params
def get_xgb_params(self):

4
tests/python/test_with_sklearn.py
View File

@ -450,6 +450,10 @@ def test_sklearn_random_state():
clf = xgb.XGBClassifier(random_state=401)
assert clf.get_xgb_params()['random_state'] == 401
random_state = np.random.RandomState(seed=403)
clf = xgb.XGBClassifier(random_state=random_state)
assert isinstance(clf.get_xgb_params()['random_state'], int)
def test_sklearn_n_jobs():
clf = xgb.XGBClassifier(n_jobs=1)