/
imbalanced.py
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/
imbalanced.py
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from imblearn.over_sampling import BorderlineSMOTE
from imblearn.ensemble import EasyEnsemble
from imblearn.pipeline import Pipeline
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler
def simple_model(X_train,y_train):
# define the methods
over = SMOTE( k_neighbors=7)
under = RandomUnderSampler()
steps = [('o', over), ('u', under)]
pipeline = Pipeline(steps=steps)
# transform the dataset
new_X_train, new_y_train = pipeline.fit_resample(X_train, y_train)
return new_X_train,new_y_train
def ensemble_model(X_train,y_train):
# define the methods
over = BorderlineSMOTE( k_neighbors=7, kind= "borderline-1")
under = EasyEnsemble(random_state=1)
steps = [('o', over), ('u', under)]
pipeline = Pipeline(steps=steps)
# transform the dataset
new_X_train, new_y_train = pipeline.fit_resample(X_train, y_train)
return new_X_train[0],new_y_train[0]