def get_result(auto_var):
file_name = get_file_name(auto_var, name_only=True).replace("_", "-")
file_path = f"./results/{file_name}.json"
if not os.path.exists(file_path):
return None
try:
with open(file_path, "r") as f:
ret = json.load(f)
except Exception as e:
print("problem with %s" % file_path)
raise e
return ret
def delete_file(auto_var):
os.unlink(get_file_name(auto_var) + '.json')
def temp_fix(auto_var):
file_name = get_file_name(auto_var)
print(file_name)
if os.path.exists("%s.json" % file_name):
with open("%s.json" % file_name, "r") as f:
ret = json.load(f)
if "tst_score" in ret:
return
else:
return
random_state = set_random_seed(auto_var)
ord = auto_var.get_var("ord")
X, y, eps_list = auto_var.get_var("dataset")
idxs = np.arange(len(X))
random_state.shuffle(idxs)
trnX, tstX, trny, tsty = X[idxs[:-200]], X[idxs[-200:]], y[idxs[:-200]], y[
idxs[-200:]]
scaler = MinMaxScaler()
trnX = scaler.fit_transform(trnX)
tstX = scaler.transform(tstX)
lbl_enc = OneHotEncoder(categories=[np.sort(np.unique(y))], sparse=False)
#lbl_enc = OneHotEncoder(sparse=False)
lbl_enc.fit(trny.reshape(-1, 1))
auto_var.set_intermidiate_variable("lbl_enc", lbl_enc)
results = []
auto_var.set_intermidiate_variable("trnX", trnX)
auto_var.set_intermidiate_variable("trny", trny)
model_name = auto_var.get_variable_value("model")
attack_name = auto_var.get_variable_value("attack")
if 'adv_rf' in model_name:
pre_model = auto_var.get_var_with_argument('model', model_name[4:])
pre_model.fit(trnX, trny)
if 'blackbox' in attack_name:
auto_var.set_intermidiate_variable("model", pre_model)
elif 'adv_nn' in model_name and 'blackbox' in attack_name:
pre_model = auto_var.get_var_with_argument('model', model_name[4:])
pre_model.fit(trnX, trny)
auto_var.set_intermidiate_variable("model", pre_model)
model = auto_var.get_var("model")
auto_var.set_intermidiate_variable("model", model)
model.fit(trnX, trny)
pred = model.predict(tstX)
ori_tstX, ori_tsty = tstX, tsty # len = 200
idxs = np.where(pred == tsty)[0]
random_state.shuffle(idxs)
augX = None
if ('adv' in model_name) or ('advPruning'
in model_name) or ('robustv2' in model_name):
assert hasattr(model, 'augX')
auto_var.set_intermidiate_variable("trnX", model.augX)
auto_var.set_intermidiate_variable("trny", model.augy)
augX, augy = model.augX, model.augy
ret['tst_score'] = (model.predict(ori_tstX) == ori_tsty).mean()
with open("%s.json" % file_name, "w") as f:
json.dump(ret, f)