def fit(self, X_train, y_train):
self.weight = GenericWeights(self.base_clf_class)
ac = AC2(self.base_clf_class)
ac.fit([X_train, y_train])
self.ac = ac
self.X_train = X_train
self.y_train = y_train
def fit(self, X_train, y_train):
self.weight = GenericWeights(self.base_clf_class)
it = Itr2(self.base_clf_class)
it.fit([X_train, y_train])
self.it = it
self.X_train = X_train
self.y_train = y_train
class CDEAC(object):
def __init__(self, base_clf_class):
self.base_clf_class = base_clf_class
def fit(self, X_train, y_train):
self.weight = GenericWeights(self.base_clf_class)
ac = AC2(self.base_clf_class)
ac.fit([X_train, y_train])
self.ac = ac
self.X_train = X_train
self.y_train = y_train
def predict(self, X_test):
class_dist = self.ac.predict_dict(X_test)
self.weight.fit(self.X_train, self.y_train, class_dist)
y_pred = self.weight.predict(X_test)
return y_pred
class CDEITR(object):
''' Combination of EM based CDE and Cost sensitive learning based on class
reweighting. '''
def __init__(self, base_clf_class):
self.base_clf_class = base_clf_class
def fit(self, X_train, y_train):
self.weight = GenericWeights(self.base_clf_class)
it = Itr2(self.base_clf_class)
it.fit([X_train, y_train])
self.it = it
self.X_train = X_train
self.y_train = y_train
def predict(self, X_test):
class_dist = self.it.predict_dict(X_test)
self.weight.fit(self.X_train, self.y_train, class_dist)
y_pred = self.weight.predict(X_test)
return y_pred
