ensemble_test_data = None
labels_pred_set = []
for directory in data_list:
print("#################################")
print("Load data saved in {0}".format(directory))
# load tracer
failure, data, tracer = load_arrangement(keyword, directory)
if not failure:
print("Load data and tracer success")
if ensemble_test_data == None:
ensemble_test_data = [
value
for _, value in sorted(zip(tracer.test, data.test.images))
]
# load label_pred
failure, labels_pred = load_labels_pred(keyword, directory)
if not failure:
print("Load labels_pred success")
temp_labels_pred = [
value for _, value in sorted(zip(tracer.test, labels_pred))
]
labels_pred_set.append(temp_labels_pred)
# load cls_true
failure, cls_true = load_cls_true(keyword, directory)
if not failure:
print("Load cls_true success")
if ensemble_cls_true == None:
ensemble_cls_true = [
value for _, value in sorted(zip(tracer.test, cls_true))
]
#-----------------------------------
ai_alice = argv[2]
ai_bob = argv[3]
work_dir = os.getcwd()
#----------------------------------------
# Load prediction 1 and true labels
print("### Prediction 1 ###")
print("AI DIR = {0}".format(ai_alice))
os.chdir(ai_alice)
data_list = glob.glob("AI*test_on*{0}".format(main_name))
ensemble_cls_true = None
alice_labels_pred_set = []
for directory in data_list:
# load tracer
failure, data, tracer = load_arrangement(main_name, directory)
# load label_pred
failure, labels_pred = load_labels_pred(main_name, directory)
if not failure:
temp_labels_pred = [
value for _, value in sorted(zip(tracer.test, labels_pred))
]
alice_labels_pred_set.append(temp_labels_pred)
# load cls_true
failure, cls_true = load_cls_true(main_name, directory)
if not failure:
if ensemble_cls_true == None:
ensemble_cls_true = [
value for _, value in sorted(zip(tracer.test, cls_true))
]
alice_labels_pred_set = np.array(alice_labels_pred_set)
alice_ensemble_labels_pred = np.mean(alice_labels_pred_set, axis=0)