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)) ]