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mix.py
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mix.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Nov 7 17:10:54 2017
@author: mong
"""
import time
import Initialize
from Evaluation_Indicators import Calculation_AUC
import pandas as pd
import numpy as np
import similarity_indicators.mixture_index
startTime = time.clock()
similarity_StartTime = time.clock()
# Matrix_similarity = similarity_indicators.Cos.ACT(MatrixAdjacency_Train)
def get_auc_mix(MatrixAdjacency_Train,MatrixAdjacency_Test, MaxNodeNum):
auc=[]
for Method in range(23):
if Method == 0:
print ('----------SIM----------共同邻居----------SIM----------')
print ('----------CN----------')
Matrix_similarity = similarity_indicators.CommonNeighbor.Cn(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 1:
print ('---------Salton-----------')
Matrix_similarity = similarity_indicators.Salton.Salton(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 2:
print ('---------Jaccavrd-----------')
Matrix_similarity = similarity_indicators.Jaccard.Jaccavrd(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 3:
print ('---------Sorenson-----------')
Matrix_similarity = similarity_indicators.Sorenson.Sorenson(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 4:
print ('---------HPI-----------')
Matrix_similarity = similarity_indicators.HPI.HPI(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 5:
print ('---------HDI-----------')
Matrix_similarity = similarity_indicators.HDI.HDI(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 6:
print ('---------LHN_I-----------')
Matrix_similarity = similarity_indicators.LHN_I.LHN_I(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 7:
print ('---------PA-----------')
Matrix_similarity = similarity_indicators.PA.PA(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 8:
print ('---------AA-----------')
Matrix_similarity = similarity_indicators.AA.AA(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 9:
print ('---------RA-----------')
Matrix_similarity = similarity_indicators.RA.RA(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 10:
print ('---------LP-----------')
Matrix_similarity = similarity_indicators.LP.LP(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 11:
print ('---------Katz-----------')
Matrix_similarity = similarity_indicators.Katz.Katz(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 12:
print ('----------SIM----------混合指标----------SIM----------')
print ('----------1----------')
Matrix_similarity = similarity_indicators.mixture_index.mixture_index_1(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 13:
print ('----------2----------')
Matrix_similarity = similarity_indicators.mixture_index.mixture_index_2(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 14:
print ('----------3----------')
Matrix_similarity = similarity_indicators.mixture_index.mixture_index_3(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method ==15:
print ('----------4----------')
Matrix_similarity = similarity_indicators.mixture_index.mixture_index_4(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 16:
print ('----------5----------')
Matrix_similarity = similarity_indicators.mixture_index.mixture_index_5(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 17:
print ('----------6----------')
Matrix_similarity = similarity_indicators.mixture_index.mixture_index_6(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 18:
print ('----------7----------')
Matrix_similarity = similarity_indicators.mixture_index.mixture_index_7(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 19:
print ('----------8----------')
Matrix_similarity = similarity_indicators.mixture_index.mixture_index_8(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 20:
print ('----------9----------')
Matrix_similarity = similarity_indicators.mixture_index.mixture_index_9(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 21:
print ('----------10----------')
Matrix_similarity = similarity_indicators.mixture_index.mixture_index_10(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
elif Method == 22:
print ('----------11----------')
Matrix_similarity = similarity_indicators.mixture_index.mixture_index_11(MatrixAdjacency_Train)
auc.append(Calculation_AUC(MatrixAdjacency_Train, MatrixAdjacency_Test, Matrix_similarity, MaxNodeNum))
else:
print ("Method Error!")
return auc
similarity_EndTime = time.clock()
print ('----------!!----------')
print ("All SimilarityTime: %f s" % (similarity_EndTime- similarity_StartTime))
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