if not failure:
print("load cls_true success")
failure, coords = load_lib.load_coords(keyword, directory)
if not failure:
print("load coords success")
#----------------------------------------
# test if the loading is successful or not
print("### data number ###")
print("length of training data set: {0}".format(len(data.train.images)))
print("length of validation data set: {0}".format(
len(data.validation.images)))
print("length of test data set: {0}".format(len(data.test.images)))
print("({0} | {1})\n".format(len(cls_pred), len(cls_true)))
# confusion matrix
print("### confusion matrix ###")
failure, cm = load_lib.confusion_matrix(cls_true, cls_pred)
if not failure:
print("confusion matrix success")
print(cm)
# print data and the corresponding shuffle tracer of the first data
print("### The first datum in dataset ###")
print("data.test.images: {0}".format(data.test.images[0]))
print("RA: {0}, DEC: {1}".format(coords.test[0][0], coords.test[0][1]))
print("shuffle tracer: {0}".format(tracer.test[0]))
print("true label: {0}".format(cls_true[0]))
print("predict label: {0}".format(cls_pred[0]))
# print data and the corresponding shuffle tracer of the last data
print("### The final datum in dataset ###")
print("data.test.images: {0}".format(data.test.images[-1]))
print("RA: {0}, DEC: {1}".format(coords.test[-1][0], coords.test[-1][1]))
print("shuffle tracer: {0}".format(tracer.test[-1]))
"test_cls_true_source_sed_{0}.npy".format(keyword))
# trace it back to the sorted list with tracers
alice_sources_sorted = [
value for _, value in sorted(zip(alice_cls_tracers, alice_sources))
]
alice_cls_pred_sorted = [
value for _, value in sorted(zip(alice_cls_tracers, alice_cls_pred))
]
alice_cls_true_sorted = [
value for _, value in sorted(zip(alice_cls_tracers, alice_cls_true))
]
alice_sources_sorted = np.array(alice_sources_sorted)
alice_cls_pred_sorted = np.array(alice_cls_pred_sorted)
alice_cls_true_sorted = np.array(alice_cls_true_sorted)
print("--- Confusion Matrix ---")
failure, cm = confusion_matrix(alice_cls_true_sorted,
alice_cls_pred_sorted)
print(cm)
#-----------------------------------
# load prediction 2
print("### Prediction 2 ###")
print("AI DIR = {0}".format(ai_bob))
os.chdir(work_dir)
os.chdir(ai_bob)
bob_sources = np.load("test_set_source_sed_{0}.npy".format(keyword))
bob_cls_pred = np.load("test_cls_pred_source_sed_{0}.npy".format(keyword))
bob_cls_tracers = np.load("test_tracer_source_sed_{0}.npy".format(keyword))
bob_cls_true = np.load("test_cls_true_source_sed_{0}.npy".format(keyword))
# trace it back to the sorted list with tracers
bob_sources_sorted = [
value for _, value in sorted(zip(bob_cls_tracers, bob_sources))
]