def main(emb_weights, training_file, validation_file, n_diagnosis_codes,
n_labels, batch_size, dropout_rate, L2_reg, n_epoch, log_eps,
n_claims, visit_size, hidden_size, model_name):
options = locals().copy()
print("Loading dataset...", file=log_f, flush=True)
test = rnn_tools.load_data(validation_file)
n_samples = NUM_ATTACK_SAMPLES
attacker = Attacker(options, emb_weights)
n_success = 0
n_fail = 0
total_node_change = 0
n_iteration = 0
saving_time = {}
attack_code_dict = {}
for i in range(n_samples):
print("-------- %d ---------" % (i), file=log_f, flush=True)
sample = test[0][i]
y = test[1][i]
print('* Processing:%d/%d samples' % (i, n_samples),
file=log_f,
flush=True)
print("* Original: " + str(sample), file=log_f, flush=True)
print(" Original label: %d" % (y), file=log_f, flush=True)
st = time.time()
maxCy, S_t, Code_S_t, success_flag, iteration = attacker.attack(
sample, y)
et = time.time()
all_t = et - st
if success_flag == 1:
n_success += 1
n_iteration += iteration
total_node_change += len(Code_S_t)
saving_time[i] = all_t
attack_code_dict[i] = Code_S_t
elif success_flag == -1:
n_fail += 1
print("* Result: ", file=log_f, flush=True)
print(Code_S_t, file=log_f, flush=True)
print(S_t, file=log_f, flush=True)
print(maxCy, file=log_f, flush=True)
print(" Nnumber of changed codes: %d" % (len(Code_S_t)),
file=log_f,
flush=True)
print(" Number of iterations for this: " + str(iteration),
file=log_f,
flush=True)
print(" Time: " + str(all_t), file=log_f, flush=True)
print("* SUCCESS Number NOW: %d " % (n_success),
file=log_f,
flush=True)
print("* Failure Number NOW: %d " % (n_fail), file=log_f, flush=True)
if n_success:
print(" Average Number of success changed codes: " +
str(float(total_node_change) / float(n_success)),
file=log_f,
flush=True)
print(" Average Number of success iterations: " +
str(float(n_iteration) / float(n_success)),
file=log_f,
flush=True)
if i % 20 == 0:
pickle.dump(
attack_code_dict,
open(
'./AttackCodes/codes_GB_%s_k=%d_t=%s_s=%d.pickle' %
(MODEL_TYPE, INDICE_K, str(TAU), SECONDS), 'wb'))
pickle.dump(
saving_time,
open(
'Each_TimeNumber/time_GB_%s_k=%d_t=%s_s=%d.pickle' %
(MODEL_TYPE, INDICE_K, str(TAU), SECONDS), 'wb'))
pickle.dump(
attack_code_dict,
open(
'./AttackCodes/codes_GB_%s_k=%d_t=%s_s=%d.pickle' %
(MODEL_TYPE, INDICE_K, str(TAU), SECONDS), 'wb'))
pickle.dump(
saving_time,
open(
'Each_TimeNumber/time_GB_%s_k=%d_t=%s_s=%d.pickle' %
(MODEL_TYPE, INDICE_K, str(TAU), SECONDS), 'wb'))
print("--- Total Success Number: " + str(n_success) + " ---",
file=log_f,
flush=True)
print("--- Total Attack Success Rate: " +
str(float(n_success) / float(n_success + n_fail)),
file=log_f,
flush=True)
print(TITLE, file=log_f, flush=True)
print(TITLE)
def main(emb_weights, training_file, validation_file, testing_file,
n_diagnosis_codes, n_labels, batch_size, dropout_rate, L2_reg,
n_epoch, log_eps, n_claims, visit_size, hidden_size, use_gpu,
model_name):
options = locals().copy()
print("Loading dataset...", file=log_f, flush=True)
test = rnn_tools.load_data(training_file, validation_file, testing_file)
n_people = NUM_ATTACK_SAMPLES
attacker = Attacker(options, emb_weights)
n_success = 89
total_node_change = n_success * 1.101123595505618
n_iteration = n_success * 1.101123595505618
saving_time = {}
attack_code_dict = {}
# ****** #
file1 = './AttackCodes/codes_Prim_sub_t=0.7_s=3600.pickle'
attack_code_dict = pickle.load(open(file1, 'rb'))
file2 = './Each_TimeNumber/time_Prim_sub_t=0.7_s=3600.pickle'
saving_time = pickle.load(open(file2, 'rb'))
# ****** #
for i in range(100, n_people):
print("-------- %d ---------" % (i), file=log_f, flush=True)
person = test[0][i]
y = test[1][i]
n_visit = len(person)
print(
'* Processing:%d/%d person, number of visit for this person: %d' %
(i, n_people, n_visit),
file=log_f,
flush=True)
print("* Original: " + str(person), file=log_f, flush=True)
print(" Original label: %d" % (y), file=log_f, flush=True)
st = time.time()
greedy_set, greedy_set_best_temp_person, best_Cy, num_changed, success_flag, iteration = attacker.attack(
person, y)
et = time.time()
all_t = et - st
if success_flag:
n_success += 1
n_iteration += iteration
total_node_change += num_changed
saving_time[i] = all_t
attack_code_dict[i] = list(greedy_set)
print("* Result: ", file=log_f, flush=True)
print(greedy_set, file=log_f, flush=True)
print(greedy_set_best_temp_person, file=log_f, flush=True)
print(best_Cy, file=log_f, flush=True)
print(" Nnumber of changed codes: %d" % (num_changed),
file=log_f,
flush=True)
print(" Number of iterations for this: " + str(iteration),
file=log_f,
flush=True)
print(" Time: " + str(all_t), file=log_f, flush=True)
print("* SUCCESS Number NOW: %d " % (n_success),
file=log_f,
flush=True)
if n_success:
print(" Average Number of success changed codes: " +
str(float(total_node_change) / float(n_success)),
file=log_f,
flush=True)
print(" Average Number of success iterations: " +
str(float(n_iteration) / float(n_success)),
file=log_f,
flush=True)
if i % 20 == 0:
pickle.dump(
attack_code_dict,
open(
'./AttackCodes/codes_Prim_%s_t=%s_s=%d.pickle' %
(MODEL_TYPE, str(TAU), SECONDS), 'wb'))
pickle.dump(
saving_time,
open(
'./Each_TimeNumber/time_Prim_%s_t=%s_s=%d.pickle' %
(MODEL_TYPE, str(TAU), SECONDS), 'wb'))
pickle.dump(
attack_code_dict,
open(
'./AttackCodes/codes_Prim_%s_t=%s_s=%d.pickle' %
(MODEL_TYPE, str(TAU), SECONDS), 'wb'))
pickle.dump(
saving_time,
open(
'./Each_TimeNumber/time_Prim_%s_t=%s_s=%d.pickle' %
(MODEL_TYPE, str(TAU), SECONDS), 'wb'))
print("--- Total Success Number: " + str(n_success) + " ---",
file=log_f,
flush=True)
print(TITLE)
print(TITLE, file=log_f, flush=True)
def main(emb_weights, training_file, validation_file,
testing_file, n_diagnosis_codes, n_labels,
batch_size, dropout_rate,
L2_reg, n_epoch, log_eps, n_claims, visit_size, hidden_size,
use_gpu, model_name):
options = locals().copy()
print("Loading dataset...", file=log_f, flush=True)
test = rnn_tools.load_data(training_file, validation_file, testing_file)
n_people = NUM_ATTACK_SAMPLES
attacker = Attacker(options, emb_weights)
n_success = 0
n_fail = 0
total_node_change = 0
n_iteration = 0
saving_time = {}
attack_code_dict = {}
# # ****** #
# file1 = './AttackCodes/codes_QL_sub_k=10_t=0.5_s=180.pickle'
#
# attack_code_dict = pickle.load(open(file1, 'rb'))
#
# file2 = './Each_TimeNumber/time_QL_sub_k=10_t=0.5_s=180.pickle'
#
# saving_time = pickle.load(open(file2, 'rb'))
# # ****** #
for i in range(n_people):
print("-------- %d ---------" % (i), file=log_f, flush=True)
person = test[0][i]
y = test[1][i]
n_visit = len(person)
print('* Processing:%d/%d person, number of visit for this person: %d' % (i, n_people, n_visit), file=log_f, flush=True)
print("* Original: "+str(person), file=log_f, flush=True)
print(" Original label: %d" % (y), file=log_f, flush=True)
st = time.time()
changed_person, score, num_changed, success_flag, iteration, changed_pos = attacker.attack(person, y)
et = time.time()
all_t = et-st
if success_flag == 1:
n_success += 1
n_iteration += iteration
total_node_change += num_changed
saving_time[i] = all_t
attack_code_dict[i] = list(changed_pos)
elif success_flag == -1:
n_fail += 1
print("* Result: ", file=log_f, flush=True)
print(changed_pos, file=log_f, flush=True)
print(changed_person, file=log_f, flush=True)
print(score, file=log_f, flush=True)
print(" Nnumber of changed codes: %d" % (num_changed), file=log_f, flush=True)
print(" Number of iterations for this: " + str(iteration), file=log_f, flush=True)
print(" Time: "+str(all_t), file=log_f, flush=True)
print("* SUCCESS Number NOW: %d " % (n_success), file=log_f, flush=True)
print("* Failure Number NOW: %d " % (n_fail), file=log_f, flush=True)
if n_success:
print(" Average Number of success changed codes: " + str(float(total_node_change)/float(n_success)), file=log_f, flush=True)
print(" Average Number of success iterations: " + str(float(n_iteration) / float(n_success)), file=log_f, flush=True)
if i % 20 == 0:
pickle.dump(attack_code_dict, open('./AttackCodes/codes_QL_%s_k=%d_t=%s_s=%d.pickle' % (MODEL_TYPE, INDICE_K, str(TAU), SECONDS), 'wb'))
pickle.dump(saving_time, open('./Each_TimeNumber/time_QL_%s_k=%d_t=%s_s=%d.pickle' % (MODEL_TYPE, INDICE_K, str(TAU), SECONDS), 'wb'))
pickle.dump(attack_code_dict, open('./AttackCodes/codes_QL_%s_k=%d_t=%s_s=%d.pickle' % (MODEL_TYPE, INDICE_K, str(TAU), SECONDS), 'wb'))
pickle.dump(saving_time, open('./Each_TimeNumber/time_QL_%s_k=%d_t=%s_s=%d.pickle' % (MODEL_TYPE, INDICE_K, str(TAU), SECONDS), 'wb'))
print("--- Total Success Number: " + str(n_success) + " ---", file=log_f, flush=True)
print("--- Total Attack Success Rate: " + str(float(n_success) / float(n_success + n_fail)), file=log_f, flush=True)
print(TITLE, file=log_f, flush=True)
print(TITLE)