def main(nome,nTest=None,K_0=20,K_1=10,K_2=5,K_3=3,K_4=20,dataset='ml-100k',path='/home/matteo/Desktop/DataMining/ml-100k/',X=0,Y=0):
if dataset == 'ml-100k':
path='/home/matteo/Desktop/DataMining/ml-100k/'
X=1682
Y=943
elif dataset == 'ml-1m':
path='/home/matteo/Desktop/DataMining/ml-1m/'
X=3952
Y=6040
elif dataset == 'yelp':
path='/home/matteo/Desktop/DataMining/yelp_dataset_academic/'
X=13490
Y=130873
PATH = __initData(path,nome,dataset,K_0,nTest)
print PATH
# ############ Costruzione dataset SENZA ITEM con MINIMO RW 5 ############
# #Seleziono tutto il dataset come data training (dataset = 0)
# __WriteMatrixCF__(0,path,PATH,X,Y)
# #Estraggo la lista degli oggetti
# Item = __getMatrixCF_ITEM__(PATH,X)
# #Estraggo la lista degli id degli oggetti con meno di 5 rw
# Itemlist = __listaItemEliminati__(Item,5)
# #Ricalcolo data-set e training-set sulla base della percentuale K_0, tutti le rw contenenti
# # gli oggetti in Itemlist saranno eliminate
# __WriteMatrixCF__(K_0,path,PATH,X,Y,Itemlist)
#
# #Istanzio le liste di utenti per i dati di training e test set
# User = __getMatrixCF__(PATH)
# UserTest = __getMatrixCF_TESTSET__(PATH)
#
# #########################################################################
############ Costruzione dataset sulla base del parametro K_0 ############
__WriteMatrixCF__(K_0,path,PATH,X,Y)
Item = __getMatrixCF_ITEM__(PATH,X)
User = __getMatrixCF__(PATH)
UserTest = __getMatrixCF_TESTSET__(PATH)
##########################################################################
print "----------------"
#Calcolo/Leggo la matrice di Similarità
SimMatrix = __simil_UxU_ObjFull__(User,K_4,Y,PATH,Written=True)
#Lancio il Racommander System sul dataset
__addNote(path,__recSystemObjUxU__(User,UserTest,SimMatrix,PATH,K_1,K_2,K_3))
print "----------------"
def main(nome,
nTest=None,
K_0=20,
K_1=10,
K_2=5,
K_3=3,
K_4=20,
dataset='ml-100k',
path='/home/matteo/Desktop/DataMining/ml-100k/',
X=0,
Y=0):
if dataset == 'ml-100k':
path = '/home/matteo/Desktop/DataMining/ml-100k/'
X = 1682
Y = 943
elif dataset == 'ml-1m':
path = '/home/matteo/Desktop/DataMining/ml-1m/'
X = 3952
Y = 6040
elif dataset == 'yelp':
path = '/home/matteo/Desktop/DataMining/yelp_dataset_academic/'
X = 13490
Y = 130873
PATH = __initData(path, nome, dataset, K_0, nTest)
print PATH
# ############ Costruzione dataset SENZA ITEM con MINIMO RW 5 ############
# #Seleziono tutto il dataset come data training (dataset = 0)
# __WriteMatrixCF__(0,path,PATH,X,Y)
# #Estraggo la lista degli oggetti
# Item = __getMatrixCF_ITEM__(PATH,X)
# #Estraggo la lista degli id degli oggetti con meno di 5 rw
# Itemlist = __listaItemEliminati__(Item,5)
# #Ricalcolo data-set e training-set sulla base della percentuale K_0, tutti le rw contenenti
# # gli oggetti in Itemlist saranno eliminate
# __WriteMatrixCF__(K_0,path,PATH,X,Y,Itemlist)
#
# #Istanzio le liste di utenti per i dati di training e test set
# User = __getMatrixCF__(PATH)
# UserTest = __getMatrixCF_TESTSET__(PATH)
#
# #########################################################################
############ Costruzione dataset sulla base del parametro K_0 ############
__WriteMatrixCF__(K_0, path, PATH, X, Y)
Item = __getMatrixCF_ITEM__(PATH, X)
User = __getMatrixCF__(PATH)
UserTest = __getMatrixCF_TESTSET__(PATH)
##########################################################################
print "----------------"
#Calcolo/Leggo la matrice di Similarità
SimMatrix = __simil_UxU_ObjFull__(User, K_4, Y, PATH, Written=True)
#Lancio il Racommander System sul dataset
__addNote(
path,
__recSystemObjUxU__(User, UserTest, SimMatrix, PATH, K_1, K_2, K_3))
print "----------------"
def main(nome,
test,
nTest=None,
dataset='ml-100k',
path='/home/matteo/Desktop/DataMining/ml-100k/',
X=0,
Y=0):
if dataset == 'ml-100k':
path = '/home/matteo/Desktop/DataMining/ml-100k/'
X = 1682
Y = 943
elif dataset == 'ml-1m':
path = '/home/matteo/Desktop/DataMining/ml-1m/'
X = 3952
Y = 6040
elif dataset == 'yelp':
path = '/home/matteo/Desktop/DataMining/yelp_dataset_academic/'
X = 13490
Y = 130873
PATH = __initData(path, nome, dataset, test, nTest)
print PATH
#__addNote(path,'prova note')
__WriteMatrixCF__(test, path, PATH, X, Y)
Item = __getMatrixCF_ITEM__(PATH, X)
User = __getMatrixCF__(PATH)
UserTest = __getMatrixCF_TESTSET__(PATH)
#
# print "----------------"
#
# SimMatrix = __simil_UxU_ObjFull__(User,test,Y,PATH,Written=False)
#
#
# __addNote(path,__recSystemObjUxU__(test,User,UserTest,SimMatrix,Y,PATH))
#
# print "----------------"
#
# SimiliIxI = __simil_IxI_ObjFull__(Item,test,X,PATH,Written=False)
#
# __addNote(path,__recSystemObjIxI__(test,User,UserTest,Item,SimiliIxI,Y,PATH))
#
#
# print "----------------"
#
SimiliIxI2 = __simil_IxI_ObjFull2__(Item, test, X, PATH, Written=False)
__addNote(
path,
__recSystemObjIxI__(test, User, UserTest, Item, SimiliIxI2, Y, PATH))
#calcolo con item in meno del dataset su base percentile
# __WriteMatrixCF__(test,path,PATH,X,Y)
# Item = __getMatrixCF_ITEM__(PATH,X)
# Itemlist = __listaItemEliminati__(Item,13)
# __WriteMatrixCF__(test,path,PATH,X,Y,Itemlist)
# Item = __getMatrixCF_ITEM__(PATH,X)
# User = __getMatrixCF__(PATH)
# UserTest = __getMatrixCF_TESTSET__(PATH)
print "----------------"
# SimMatrix = __simil_UxU_ObjFull__(User,test,Y,PATH,Written=False)
# __addNote(path,__recSystemObjUxU__(test,User,UserTest,SimMatrix,Y,PATH))
# print "----------------"
# SimMatrix = __simil_UxU_ObjFull2__(User,test,Y,PATH,Written=False)
# __addNote(path,__recSystemObjUxU__(test,User,UserTest,SimMatrix,Y,PATH))
# print "----------------"
# SimiliIxI = __simil_IxI_ObjFull__(Item,test,X,PATH,Written=False)
# __addNote(path,__recSystemObjIxI__(test,User,UserTest,Item,SimiliIxI,Y,PATH))
# print "----------------"
#SimiliIxI2 = __simil_IxI_ObjFull2__(Item,test,X,PATH,Written=False)
#__addNote(path,__recSystemObjIxI__(test,User,UserTest,Item,SimiliIxI2,Y,PATH))
#if __name__ == "__main__":
#main('TEST_FullData',10,0,dataset='ml-1m')
def main(nome,test,nTest=None,dataset='ml-100k',path='/home/matteo/Desktop/DataMining/ml-100k/',X=0,Y=0):
if dataset == 'ml-100k':
path='/home/matteo/Desktop/DataMining/ml-100k/'
X=1682
Y=943
elif dataset == 'ml-1m':
path='/home/matteo/Desktop/DataMining/ml-1m/'
X=3952
Y=6040
elif dataset == 'yelp':
path='/home/matteo/Desktop/DataMining/yelp_dataset_academic/'
X=13490
Y=130873
PATH = __initData(path,nome,dataset,test,nTest)
print PATH
#__addNote(path,'prova note')
__WriteMatrixCF__(test,path,PATH,X,Y)
Item = __getMatrixCF_ITEM__(PATH,X)
User = __getMatrixCF__(PATH)
UserTest = __getMatrixCF_TESTSET__(PATH)
#
# print "----------------"
#
# SimMatrix = __simil_UxU_ObjFull__(User,test,Y,PATH,Written=False)
#
#
# __addNote(path,__recSystemObjUxU__(test,User,UserTest,SimMatrix,Y,PATH))
#
# print "----------------"
#
# SimiliIxI = __simil_IxI_ObjFull__(Item,test,X,PATH,Written=False)
#
# __addNote(path,__recSystemObjIxI__(test,User,UserTest,Item,SimiliIxI,Y,PATH))
#
#
# print "----------------"
#
SimiliIxI2 = __simil_IxI_ObjFull2__(Item,test,X,PATH,Written=False)
__addNote(path,__recSystemObjIxI__(test,User,UserTest,Item,SimiliIxI2,Y,PATH))
#calcolo con item in meno del dataset su base percentile
# __WriteMatrixCF__(test,path,PATH,X,Y)
# Item = __getMatrixCF_ITEM__(PATH,X)
# Itemlist = __listaItemEliminati__(Item,13)
# __WriteMatrixCF__(test,path,PATH,X,Y,Itemlist)
# Item = __getMatrixCF_ITEM__(PATH,X)
# User = __getMatrixCF__(PATH)
# UserTest = __getMatrixCF_TESTSET__(PATH)
print "----------------"
# SimMatrix = __simil_UxU_ObjFull__(User,test,Y,PATH,Written=False)
# __addNote(path,__recSystemObjUxU__(test,User,UserTest,SimMatrix,Y,PATH))
# print "----------------"
# SimMatrix = __simil_UxU_ObjFull2__(User,test,Y,PATH,Written=False)
# __addNote(path,__recSystemObjUxU__(test,User,UserTest,SimMatrix,Y,PATH))
# print "----------------"
# SimiliIxI = __simil_IxI_ObjFull__(Item,test,X,PATH,Written=False)
# __addNote(path,__recSystemObjIxI__(test,User,UserTest,Item,SimiliIxI,Y,PATH))
# print "----------------"
#SimiliIxI2 = __simil_IxI_ObjFull2__(Item,test,X,PATH,Written=False)
#__addNote(path,__recSystemObjIxI__(test,User,UserTest,Item,SimiliIxI2,Y,PATH))
#if __name__ == "__main__":
#main('TEST_FullData',10,0,dataset='ml-1m')
