def perform_under(**kwargs):
# Apply the undersampling according to the opf-us variant represented by the opf_us_obj
opf_us_obj = kwargs['us_obj']
X = kwargs['X']
y = kwargs['y']
X_test = kwargs['X_test']
y_test = kwargs['y_test']
f = kwargs['fold']
ds = kwargs['ds']
valid = kwargs['valid']
start_time = time()
X_res, y_res = opf_us_obj.fit_resample(X, y, valid)
end_time = time() - start_time
approach = opf_us_obj.__class__.__name__
common = COMMON()
common.saveTimeOnly(ds, f, approach, end_time, 'Results')
def perform_under(**kwargs):
# Apply the hybrid approach according to the opf-us variant represented by the hybrid_obj
hybrid_obj = kwargs['hybrid_obj']
X = kwargs['X']
y = kwargs['y']
X_test = kwargs['X_test']
y_test = kwargs['y_test']
f = kwargs['fold']
ds = kwargs['ds']
valid = kwargs['valid']
start_time = time()
all_x, all_y = hybrid_obj.fit_resample(X, y)
end_time = time() - start_time
approach = hybrid_obj.__class__.__name__
common = COMMON()
# Save the results of the oversampling
common.saveTimeOnly(ds, f, approach, end_time, 'Results')
def perform_over(**kwargs):
o2pf_obj = kwargs['o2pf_obj']
X = kwargs['X']
y = kwargs['y']
X_valid = kwargs['X_valid']
y_valid = kwargs['y_valid']
X_test = kwargs['X_test']
y_test = kwargs['y_test']
ds = kwargs['ds']
f = kwargs['f']
k_max = kwargs['k_max']
common = COMMON()
approach = o2pf_obj.__class__.__name__
best_k = 5
start_time = time()
o2pf_obj.k_max = best_k
all_x, all_y = o2pf_obj.fit_resample(X, y)
end_time = time() - start_time
common.saveTimeOnly(ds, f, approach, end_time, 'Results')