def __init__(self,
imbalance_upsampling=None,
class_weight=None,
method=None,
random_state=10,
log=None):
MlModelCommon.__init__(self,
imbalance_upsampling=imbalance_upsampling,
class_weight=class_weight,
method=method,
log=log)
if method == "Bagging":
model = DecisionTreeClassifier(class_weight=class_weight,
min_samples_split=20,
random_state=99)
self.ensemble_method = BaggingClassifier(base_estimator=model,
n_estimators=10,
random_state=random_state)
elif method == "Adaptive Boosting":
model = DecisionTreeClassifier(class_weight=class_weight,
min_samples_split=20,
random_state=99)
self.ensemble_method = AdaBoostClassifier(
base_estimator=model,
n_estimators=50,
random_state=random_state)
else:
self.ensemble_method = None
DecisionTreeClassifier.__init__(self,
class_weight=class_weight,
min_samples_split=20,
random_state=99)
def __init__(self, **kwargs):
DecisionTreeClassifier.__init__(self, **kwargs)
return
def __init__(self,threshold=1,ll_ranking=False,**kwargs):
DT.__init__(self,**kwargs)
BaseClassifier.__init__(self,threshold=threshold,ll_ranking=ll_ranking)
def __init__(self):
"""
constructor
"""
DecisionTreeClassifier.__init__(self, max_depth=5)
def __init__(self, dataset, use_relevance=False):
DecisionTreeClassifier.__init__(self)
self.n_inputs = dataset.data.shape[1]
self.n_targets = len(dataset.target_names)
self.use_relevance = use_relevance