k += 1
for mod in range(model_no):
sheets[ind].write(mod + 1, 0, 'model_{}'.format(mod))
for train_index, test_index in skf.split(atribute, output):
lst = [
models.Fair_PR(sensitive=prot, class_attr='labels', eta=eta[ind]),
models.Fair_meta(sensitive=prot, tau=eta[ind]),
models.Fair_DI_NN(sensitive=prot,
inp_size=inp,
num_layers_y=3,
step_y=1.5,
repair_level=eta[ind]),
models.Fair_DI_RF(sensitive=prot, repair_level=eta[ind]),
models.Fair_rew_NN(un_gr,
pr_gr,
inp_size=inp,
num_layers_y=3,
step_y=1.5),
models.Fair_rew_RF(un_gr, pr_gr),
models.FAD_class(input_size=inp,
num_layers_z=3,
num_layers_y=3,
step_z=1.5,
step_y=1.5),
models.FAIR_scalar_class(input_size=inp,
num_layers_w=3,
step_w=1.5,
num_layers_A=2,
k += 1
row = 1
for a in alpha:
ind = eta[iteracija]
iteracija += 1
lst = [
models.Fair_PR(sensitive=prot, class_attr='labels', eta=ind),
models.Fair_DI_NN(sensitive=prot,
inp_size=inp,
num_layers_y=3,
step_y=1.5,
repair_level=ind),
models.Fair_DI_RF(sensitive=prot, repair_level=ind),
models.Fair_rew_NN(un_gr,
pr_gr,
inp_size=inp,
num_layers_y=3,
step_y=1.5),
models.Fair_rew_RF(un_gr, pr_gr),
models.FAD_class(input_size=inp,
num_layers_z=3,
num_layers_y=3,
step_z=1.5,
step_y=1.5),
models.FAIR_scalar_class(input_size=inp,
num_layers_w=3,
step_w=1.5,
num_layers_A=2,
