def __init__(self, imbalance_upsampling=None, class_weight=None, method=None, random_state=1, log=None):
"""
Construtor
:param imbalance_upsampling: Use upsampling to compensate imbalanced dataset
:param class_weight: Use class_weight to compensate imbalanced dataset
:param method: [Optional] Ensemble method
:param random_state: Random state
:param log: Log
"""
MlModelCommon.__init__(self,
imbalance_upsampling=imbalance_upsampling,
class_weight=class_weight,
method=method,
log=log)
#
# GaussianNB does not support class_weight
#
if method == "Bagging":
model = GaussianNB()
self.ensemble_method = BaggingClassifier(base_estimator=model,
n_estimators=100,
random_state=random_state)
elif method == "Adaptive Boosting":
model = GaussianNB()
self.ensemble_method = AdaBoostClassifier(base_estimator=model,
n_estimators=100,
random_state=random_state)
else:
self.ensemble_method = None
GaussianNB.__init__(self)
def __init__(self, priors=None):
GaussianNB.__init__(self, priors)
def __init__(self):
GaussianNB.__init__(self)
self.equivalent={'means':'theta_',
'stddevs':'sigma_',
'fraction':'class_prior_'}
def __init__(self, priors=None):
_skGaussianNB.__init__(self, priors)
BaseWrapperClf.__init__(self)
