def cross_validation(NetFile, NetName, fold=10):
methods = [
'CN', 'Salton', 'Jaccard', 'Sorenson', 'HPI', 'HDI', 'LHN-I', 'PA',
'AA', 'RA', 'LP', 'Katz'
]
auc_mix = np.zeros(23)
for i in range(10):
MatrixAdjacency_Net, MaxNodeNum = Initialize.Init(NetFile)
MatrixAdjacency_Train, MatrixAdjacency_Test = Initialize.Divide(
NetFile, MatrixAdjacency_Net, MaxNodeNum, NetName)
auc = get_auc_mix(MatrixAdjacency_Train, MatrixAdjacency_Test,
MaxNodeNum)
auc = np.array(auc)
auc_mix += auc
auc_mix = (0.1) * auc_mix
auc_mix = pd.Series(auc_mix, index=methods + (list(range(1, 12))))
auc_mix.name = 'auc'
return auc_mix
def cross_validation(NetFile, NetName, fold=10):
auc_val = np.zeros(12)
methods = [
'CN', 'Salton', 'Jaccard', 'Sorenson', 'HPI', 'HDI', 'LHN-I', 'PA',
'AA', 'RA', 'LP', 'Katz'
]
for i in range(10):
MatrixAdjacency_Net, MaxNodeNum = Initialize.Init(NetFile)
MatrixAdjacency_Train, MatrixAdjacency_Test = Initialize.Divide(
NetFile, MatrixAdjacency_Net, MaxNodeNum, NetName)
auc = get_auc_all(MatrixAdjacency_Train, MatrixAdjacency_Test,
MaxNodeNum)
auc = np.array(auc)
auc_val += auc
auc_val = (0.1) * auc_val
auc_val = pd.Series(auc_val, index=methods)
auc_val.name = 'auc'
return auc_val
NetName = 'INT'
elif Set == 6:
NetFile = u'Data/USAir.txt'
NetName = 'USAir'
else:
print 'Input Error'
print "\nLink Prediction start：\n"
TrainFile_Path = 'Data\\'+NetName+'\\Train.txt'
if os.path.exists(TrainFile_Path):
Train_File = 'Data\\'+NetName+'\\Train.txt'
Test_File = 'Data\\'+NetName+'\\Test.txt'
MatrixAdjacency_Train,MatrixAdjacency_Test,MaxNodeNum = Initialize.Init2(Test_File, Train_File)
else:
MatrixAdjacency_Net,MaxNodeNum = Initialize.Init(NetFile)
MatrixAdjacency_Train,MatrixAdjacency_Test = Initialize.Divide(NetFile, MatrixAdjacency_Net, MaxNodeNum,NetName)
#计算相似性矩阵
# READEME = '''\nPlease choose a method:
# CN 0
# Salton 1
# Jaccard 2
# Sorenson 3
# HPI 4
# HDI 5
# LHN-I 6
# PA 7
# AA 8
# RA 9
# LP 10
# Katz 11
