def kfold(inputs,outputs,config):
n_sp = 10
kfolder = KFold(n_splits=n_sp, shuffle=True)
fit_acum = 0
acc_acum = 0
time_average_acum = 0
generations_acum = 0
fitn = []
accu = []
tim = []
gen = []
genomes = []
outputs = np.asarray(outputs)
inputs = np.asarray(inputs)
folds = 0
for train_index, test_index in kfolder.split(inputs):
print('--------------------------------------------------------------')
print(" KFOLD: " + str(folds))
#print(" TRAIN: ", train_index, " TEST: ", test_index)
in_train, in_test = inputs[train_index], inputs[test_index]
out_train, out_test = outputs[train_index], outputs[test_index]
in_train = in_train.tolist()
in_test = in_test.tolist()
out_train = out_train.tolist()
out_test = out_test.tolist()
test_set = list(zip(in_test,out_test))
result = run(in_train, out_train,config)
time_average, generations = result['evol']
n_test = len(in_test)
fit = kfold_fit(result['net'], test_set, n_test)
acc = kfold_acc(result['net'], test_set, n_test)
fit_acum += fit
acc_acum += acc
time_average_acum += time_average
generations_acum += generations
fitn.append(fit)
accu.append(acc)
tim.append(time_average)
gen.append(generations)
genomes.append(result['genome'])
print(" FOLD Fit: ", fit)
print(" FOLD Acc: ", acc)
print(" FOLD Time: ", time_average)
folds += 1
final_fit = fit_acum / folds
final_acc = acc_acum / folds
final_time = time_average_acum / folds
final_generations = generations_acum / folds
print('--------------------------------------------------------------')
print(" Fitness list: ", str(fitn))
print(" Accuracy list: ", str(accu))
print(" Average Time list: ", str(tim))
print(" Generations list: ", str(gen))
print(" Fitness: ", final_fit)
print(" Accuracy: ", final_acc)
print(" Average Time: ", final_time)
print(" Generations: ", final_generations)
return {'f_fit': final_fit, 'f_acc': final_acc, 'f_time': final_time, 'f_generations': final_generations, 'fit_list': fitn, 'accu_list': accu, 'time_list' : tim, 'gen_list': gen, 'genomes': genomes }
def kfold(inputs, outputs, config_file):
n_sp = 10
kfolder = KFold(n_splits=n_sp, shuffle=True)
acc_acum = 0
time_average_acum = 0
generations_acum = 0
outputs = np.asarray(outputs)
inputs = np.asarray(inputs)
folds = 0
for train_index, test_index in kfolder.split(inputs):
print(" NEW KFOLD ")
#print(" TRAIN: ", train_index, " TEST: ", test_index)
in_train, in_test = inputs[train_index], inputs[test_index]
out_train, out_test = outputs[train_index], outputs[test_index]
in_train = in_train.tolist()
in_test = in_test.tolist()
out_train = out_train.tolist()
out_test = out_test.tolist()
test_set = list(zip(in_test, out_test))
winner, evol = run(in_train, out_train, config_file)
time_average, generations = evol
n_test = len(in_test)
acc = kfold_acc(winner, test_set, n_test)
acc_acum += acc
time_average_acum += time_average
generations_acum += generations
print(" FOLD Acc: ", acc)
print(" FOLD Time: ", time_average)
folds += 1
final_acc = acc_acum / folds
final_time = time_average_acum / folds
final_generations = generations_acum / folds
print(" Accuracy: ", final_acc)
print(" Average Time: ", final_time)
print(" Generations: ", final_generations)
return final_time, final_generations
fitness_acum = 0
acc_acum = 0
diabetes_output = np.asarray(diabetes_output)
diabetes_inputs = np.asarray(diabetes_inputs)
folds = 0
for train_index, test_index in kfolder.split(diabetes_inputs):
print(" NEW KFOLD ")
#print(" TRAIN: ", train_index, " TEST: ", test_index)
in_train, in_test = diabetes_inputs[train_index], diabetes_inputs[
test_index]
out_train, out_test = diabetes_output[train_index], diabetes_output[
test_index]
in_train = in_train.tolist()
in_test = in_test.tolist()
out_train = out_train.tolist()
out_test = out_test.tolist()
test_set = list(zip(in_test, out_test))
winner = run(in_train, out_train)
n_test = len(in_test)
fold_fitness = kfold_fitness(winner, test_set, n_test)
acc = kfold_acc(winner, test_set, n_test)
acc_acum += acc
fitness_acum += fold_fitness
print(" FOLD FITNESS : ", fold_fitness)
print(" FOLD ACC : ", acc)
folds += 1
final_fitness = fitness_acum / folds
final_acc = acc_acum / folds
print(" FITNESS TOTAL : ", final_fitness)
print(" ACC TOTAL : ", final_acc)
