def autofis_onecv(file_zip, file_train, file_test, parameters):
# General parameters
t_norm = parameters[3]
max_size_of_premise = parameters[5]
association_method = parameters[11]
aggregation_method = parameters[12]
# Gathering parameters
# Formulation parameters:
par_area, par_over, par_pcd = toolfis.get_formulation_parameters(parameters)
# 1. Lecture & Fuzzification
out1 = toolfis.lecture_fuz_one_cv(file_zip, file_train, file_test, parameters)
ux_train, cbin_train = out1[0]
ux_test, cbin_test = out1[1]
num_premises_by_attribute, premises_by_attribute, ref_attributes, premises_contain_negation = out1[2]
freq_classes = out1[3]
report = [] # To save our results
try:
# 3. Formulation
f2 = Formulation(ux_train, cbin_train, ref_attributes, premises_by_attribute,
num_premises_by_attribute, premises_contain_negation)
# Inputs given by user
arbol = f2.gen_ARB(max_size_of_premise, t_norm, par_area, par_over, par_pcd)
status = [0 if not i[0] else 1 for i in arbol]
sum_status = sum(status)
if sum_status != len(arbol):
if sum_status == 0:
raise ValueError("Error in Formulation Module. Any premise survived. "
"Sorry, you can not continue in the next stage."
"\nTry to change the configuration")
else:
arb = [i for i in arbol if i[0]]
arbol, arb = arb, arbol
number_classes = cbin_train.shape[1]
report.append("\nFormulation:\n-----------------")
report.append("Elementos acorde a la profundidad " + str(len(arbol)) + " del arbol")
for i in range(len(arbol)):
report.append('Profundidad ' + str(i + 1) + ': ' + str(arbol[i][1].shape))
# print 'Profundidad ' + str(i + 1) + ': ' + str(arbol[i][1].shape)
# 4. Association: ex-Division
f3 = Association(arbol, cbin_train)
premises_ux_by_class = f3.division(association_method)
status = [0 if not i[0] else 1 for i in premises_ux_by_class]
if sum(status) != number_classes:
raise ValueError("Error in Division Module. Some classes did not get premises. "
"Sorry, you can not continue in the next stage."
"\nTry to change the configuration")
# 5. Aggregation:
f4 = Aggregation(premises_ux_by_class, cbin_train)
output_aggregation = f4.aggregation(aggregation_method)
premises_weights_names = output_aggregation[0]
estimation_classes = output_aggregation[1]
status = [0 if not i[0] else 1 for i in premises_weights_names]
if sum(status) != number_classes:
raise ValueError("Error in Aggregation Module. Some classes did not get premises. "
"Sorry, you can not continue in the next stage."
"\nTry to change the configuration")
final_premises_classes = []
report.append("\n\nPremises:\n=========")
for i in range(len(premises_weights_names)):
report.append("Premises of Class " + str(i) + ": " + str(premises_weights_names[i][0]))
final_premises_classes.append(premises_weights_names[i][0])
report.append("weights_" + str(i) + ": " + str(premises_weights_names[i][1].T))
# 6. Decision:
f5 = Decisions(estimation_classes, freq_classes)
train_bin_prediction = f5.dec_max_pert()
# 7. Evaluation
f6 = Evaluation(premises_weights_names, final_premises_classes, freq_classes)
metrics_train = f6.eval_train(cbin_train, train_bin_prediction)
metrics_test = f6.eval_test(cbin_test, ux_test, t_norm)
report.append("\nEvaluation Training:\n---------------------------")
report.append("Accuracy on train dataset: " + str(metrics_train[0]))
report.append("AUC in train dataset: " + str(metrics_train[1]))
report.append("Recall: " + str(metrics_train[3]))
report.append('Confusion matrix:\n' + str(metrics_train[2]))
report.append("\nEvaluation Testing:\n---------------------------")
report.append("Accuracy on test dataset: " + str(metrics_test[0]))
report.append("AUC in test dataset: " + str(metrics_test[1]))
report.append("Recall: " + str(metrics_test[3]))
report.append("Confusion matrix:\n" + str(metrics_test[2]))
# Metrics to eval: accuracy_test, auc_test,
# [num_regras, total_rule_length, tamano_medio_das_regras]]
metricas = [1, [metrics_train[0], metrics_test[0], metrics_train[1], metrics_test[1], metrics_test[4]]]
except ValueError as e:
print e
report = e # .append("\n" + str(e))
metricas = [0, "No se termino el proceso, se detuvo en algun etapa"]
return report, metricas
