else:
core_metrics[system] = hist_cm.getFECoreMetrics(system)
params = np.arange(0.0, 20.0, 0.5)/100. if args.antipattern == 'god_class' else np.arange(100., 300., 5)/100.
# Initialize progressbar
bar = progressbar.ProgressBar(maxval=len(params), \
widgets=['Tuning HIST parameters for ' + args.test_system + ': ' ,progressbar.Percentage()])
bar.start()
perfs = []
for i, alpha in enumerate(params):
bar.update(i)
overall_prediction = np.empty(shape=[0, 1])
overall_labels = np.empty(shape=[0, 1])
for system in systems:
smells = [entityName for entityName, ratio in core_metrics[system].items() if ratio[0]>alpha]
prediction = detection_utils.predictFromDetect(args.antipattern, system, smells)
overall_prediction = np.concatenate((overall_prediction, prediction), axis=0)
overall_labels = np.concatenate((overall_labels, detection_utils.getLabels(args.antipattern, system)), axis=0)
perfs.append(detection_utils.mcc(overall_prediction, overall_labels))
bar.finish()
output_file_path = os.path.join(ROOT_DIR, 'experiments', 'tuning', 'results', 'hist', args.antipattern, args.test_system + '.csv')
indexes = np.argsort(np.array(perfs))
with open(output_file_path, 'w') as file:
file.write("Alpha;MCC\n")
for i in reversed(indexes):
file.write("{0:.3f};{1}\n".format(params[i], perfs[i]))
def predict(systemName):
return detection_utils.predictFromDetect('feature_envy', systemName, detect(systemName))
def predict(systemName):
return detection_utils.predictFromDetect('god_class', systemName,
detect(systemName))
for fdp in range(1, 6)]
# Initialize progressbar
bar = progressbar.ProgressBar(maxval=len(params), \
widgets=['Tuning InCode parameters for ' + args.test_system + ': ' ,progressbar.Percentage()])
bar.start()
perfs = []
count = 0
for atfd, laa, fdp in params:
count += 1
bar.update(count)
overall_prediction = np.empty(shape=[0, 1])
for system in systems:
prediction = detection_utils.predictFromDetect(
'feature_envy', system,
incode.detect_with_params(system, atfd, laa, fdp))
overall_prediction = np.concatenate(
(overall_prediction, prediction), axis=0)
perfs.append(detection_utils.mcc(overall_prediction, overall_labels))
bar.finish()
output_file_path = os.path.join(ROOT_DIR, 'experiments', 'tuning',
'results', 'incode',
args.test_system + '.csv')
indexes = np.argsort(np.array(perfs))
with open(output_file_path, 'w') as file:
file.write("ATFD;LAA;FDP;MCC\n")
for i in reversed(indexes):
atfd, laa, fdp = params[i]