if __name__ == '__main__': # NetData = np.loadtxt("./Datasets/raw/wikivote.txt") f = open("./Datasets/raw/wikivote.txt", "r") # train, test = train_test_split.k_fold_split(f, 10) # sim = np.dot(train, train) # score = AUC.Calculation_AUC(train, test, sim, train.shape[0]) # print(score) # train, test = train_test_split.time_based_split(f, 0.9) f_tr = open("train.txt", "r") f_te = open("test.txt", "r") train, test = train_test_split.read_from_txt(f_tr, f_te) sim_1 = np.dot(train, train) score_1 = AUC.Calculation_AUC(train, test, sim_1, train.shape[0], 10000) print(score_1) sim_2 = np.dot(np.dot(train, train.T), train) score_2 = AUC.Calculation_AUC(train, test, sim_2, train.shape[0], 10000) print(score_2) sim_3 = basic_measures.IP(train, 0.3) score_3 = AUC.Calculation_AUC(train, test, sim_3, train.shape[0], 10000) print(score_3) # n_folds = 10 # linklist = [] # train_list = [] # test_list = [] # f = open("./Datasets/temporal_sort/email-Eu-core-temporal_sorted.txt", "r") # train_list, test_list = train_test_split.train_test_split(f, 0.8) # # f_t = open("train.txt", "r")
from preprocess import train_test_split from metrics import evaluationMetric from metrics import AUC from algorithms import basic_measures import numpy as np if __name__ == '__main__': # f = open("./Datasets/raw/wikivote.txt", "r") # linklist = prehandle_dataset.gen_linklist_from_txt(f) # f = open("linklist.txt", "r") # linklist = prehandle_dataset.gen_linklist_from_txt(f) # adj_train, adj_test = train_test_split.k_fold_split(linklist, 10) network = "PB" N_exp = 5 auc = np.zeros((1, N_exp)) pre = np.zeros((1, N_exp)) for ith_exp in range(N_exp): f_tr = open("./divided_dataset/"+network+"_tr_0.9_"+str(ith_exp+1)+".txt", "r") f_te = open("./divided_dataset/"+network+"_te_0.9_"+str(ith_exp+1)+".txt", "r") adj_train, adj_test = train_test_split.read_from_txt(f_tr, f_te) sim = basic_measures.Bifan(adj_train) auc[0, ith_exp] = AUC.Calculation_AUC(adj_train, adj_test, sim, adj_train.shape[0], 10000) pre[0, ith_exp] = evaluationMetric.cal_precision(adj_train, adj_test, sim, 100) print(auc.mean()) print(pre.mean())