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

Merge pull request #220 from white1033/master

Browse Source 
*Fix XGBClassifier super()
This commit is contained in:
Tianqi Chen 2015-04-05 09:05:08 -07:00
commit e626b62daa
1 changed files with 35 additions and 36 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
wrapper/xgboost.py
View File

@ -26,7 +26,6 @@ except ImportError:
SKLEARN_INSTALLED = False SKLEARN_INSTALLED = False
__all__ = ['DMatrix', 'CVPack', 'Booster', 'aggcv', 'cv', 'mknfold', 'train'] __all__ = ['DMatrix', 'CVPack', 'Booster', 'aggcv', 'cv', 'mknfold', 'train']
if sys.version_info[0] == 3: if sys.version_info[0] == 3:
@ -632,7 +631,6 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
return bst return bst
class CVPack(object): class CVPack(object):
def __init__(self, dtrain, dtest, param): def __init__(self, dtrain, dtest, param):
self.dtrain = dtrain self.dtrain = dtrain
@ -778,6 +776,7 @@ class XGBModel(BaseEstimator):
'silent': True if self.silent == 1 else False, 'silent': True if self.silent == 1 else False,
'objective': self.objective 'objective': self.objective
} }
def get_xgb_params(self): def get_xgb_params(self):
return {'eta': self.eta, 'max_depth': self.max_depth, 'silent': self.silent, 'objective': self.objective} return {'eta': self.eta, 'max_depth': self.max_depth, 'silent': self.silent, 'objective': self.objective}
@ -790,9 +789,10 @@ class XGBModel(BaseEstimator):
testDmatrix = DMatrix(X) testDmatrix = DMatrix(X)
return self._Booster.predict(testDmatrix) return self._Booster.predict(testDmatrix)
class XGBClassifier(XGBModel, ClassifierMixin): class XGBClassifier(XGBModel, ClassifierMixin):
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True): def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True):
super().__init__(max_depth, learning_rate, n_estimators, silent, objective="binary:logistic") super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective="binary:logistic")
def fit(self, X, y, sample_weight=None): def fit(self, X, y, sample_weight=None):
y_values = list(np.unique(y)) y_values = list(np.unique(y))
@ -836,7 +836,6 @@ class XGBClassifier(XGBModel, ClassifierMixin):
classzero_probs = 1.0 - classone_probs classzero_probs = 1.0 - classone_probs
return np.vstack((classzero_probs, classone_probs)).transpose() return np.vstack((classzero_probs, classone_probs)).transpose()
class XGBRegressor(XGBModel, RegressorMixin): class XGBRegressor(XGBModel, RegressorMixin):
pass pass