X, y = loaddata(r"C:\Users\znasrullah001\Documents\project-files\PyOD\LSCP\datasets\cardio")
random_state = np.random.RandomState(0)
el = []
k_list = random_state.randint(5, 200, size=50).tolist()
for k in k_list:
el.append(LOF(k))
// create the model
lscp = LSCP(el, random_state=random_state, local_region_size=100)
// split the data into training and testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=random_state)
X_train, X_test = standardizer(X_train, X_test)
// fit and predict
lscp.fit(X_train)
scores = lscp.decision_function(X_test)
print(roc_auc_score(y_test, scores))