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,