for a in alpha:
metrics = np.zeros([model_no,8])
k = 1
for i in columns:
sheets[ind].write(0,k,i)
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_rew_RF(un_gr, pr_gr),
models.FAD_class(input_size = inp, num_layers_z = 2, num_layers_y = 2,
step_z = 1.5, step_y = 1.5),
models.FAIR_scalar_class(input_size = inp, num_layers_w = 2, step_w = 1.5,
num_layers_A = 1, step_A = 1.5, num_layers_y = 2, step_y = 1.5),
models.FAIR_betaSF_class(input_size = inp, num_layers_w = 2, step_w = 1.5,
num_layers_A = 1, step_A = 1.5, num_layers_y = 2, step_y = 1.5),
models.FAIR_Bernoulli_class(input_size = inp, num_layers_w = 2, step_w = 1.5,
num_layers_A = 1, step_A = 1.5, num_layers_y = 2, step_y = 1.5),
models.FAIR_betaREP_class(input_size = inp, num_layers_w = 2, step_w = 1.5,
num_layers_A = 1, step_A = 1.5, num_layers_y = 2, step_y = 1.5),
models.FAD_prob_class(flow_length = 2, no_sample = 32,
input_size = inp, num_layers_y = 2,
step_y = 2, step_z = 2)]
x_train, x_test = atribute.iloc[train_index,:], atribute.iloc[test_index,:]
ind = 0
for a in alpha:
metrics = np.zeros([model_no, 8])
k = 1
for i in columns:
sheets[ind].write(0, k, i)
k += 1
for mod in range(model_no):
sheets[ind].write(mod + 1, 0, 'model_{}'.format(mod))
lst = [
models.Fair_rew_RF(un_gr, pr_gr),
models.FAD_class(input_size=inp,
num_layers_z=3,
num_layers_y=2,
step_z=2,
step_y=2),
models.FAIR_scalar_class(input_size=inp,
num_layers_w=2,
step_w=2,
num_layers_A=2,
step_A=2,
num_layers_y=3,
step_y=2),
models.FAIR_betaSF_class(input_size=inp,
num_layers_w=2,
step_w=2,
num_layers_A=2,
step_A=2,
num_layers_y=3,
