def __init__(self,
alpha=1.0,
binarize=.0,
fit_prior=True,
class_prior=None):
_skBernoulliNB.__init__(self, alpha, binarize, fit_prior, class_prior)
BaseWrapperClf.__init__(self)
def __init__(self,
imbalance_upsampling=None,
class_weight=None,
method=None,
c=100.0,
random_state=1,
log=None):
MlModelCommon.__init__(self,
imbalance_upsampling=imbalance_upsampling,
class_weight=class_weight,
method=method,
log=log)
if method == "Bagging":
model = BernoulliNB()
self.ensemble_method = BaggingClassifier(base_estimator=model,
n_estimators=10,
random_state=random_state)
elif method == "Adaptive Boosting":
model = BernoulliNB()
self.ensemble_method = AdaBoostClassifier(
base_estimator=model,
n_estimators=10,
random_state=random_state)
else:
#
# BernoulliNB does not support class_weight
#
BernoulliNB.__init__(self)
self.ensemble_method = None
