print "Length of new_prefs %d"%(len(new_prefs))
print 'Doing CBR...'
sparsity.caseone_doFillUsingCBR(new_prefs, deleted_users)
print 'Doing Average Filling...'
sparsity.caseone_doAverageFilling(new_prefs, deleted_users)
#use the original prefs here.
print 'Calculating original item similarity...'
oitemsim = original.calculateSimilarItems(org_prefs, n=50)
#use the modified prefs here.
print 'Calculating algorithm item similarity...'
itemsim = algorithm.calculateSimilarItems(new_prefs, n=50)
print 'Calculating original ranking...'
oranking = original.getRecommendedItems(org_prefs, oitemsim,str(user))
print 'Calculating algorithm ranking...'
ranking = original.getRecommendedItems(new_prefs,itemsim,str(user))
print 'Calculating MAE for original...'
original_MAE.append(calMAE(oranking,str(user)))
print 'Calculating MAE for algorithm...'
algorithm_MAE.append(calMAE(ranking,str(user)))
print original_MAE
print algorithm_MAE
print "Length of new_prefs %d" % (len(new_prefs))
print 'Doing CBR...'
sparsity.caseone_doFillUsingCBR(new_prefs, deleted_users)
print 'Doing Average Filling...'
sparsity.caseone_doAverageFilling(new_prefs, deleted_users)
#use the original prefs here.
print 'Calculating original item similarity...'
oitemsim = original.calculateSimilarItems(org_prefs, n=50)
#use the modified prefs here.
print 'Calculating algorithm item similarity...'
itemsim = algorithm.calculateSimilarItems(new_prefs, n=50)
print 'Calculating original ranking...'
oranking = original.getRecommendedItems(org_prefs, oitemsim, str(user))
print 'Calculating algorithm ranking...'
ranking = original.getRecommendedItems(new_prefs, itemsim, str(user))
print 'Calculating MAE for original...'
original_MAE.append(calMAE(oranking, str(user)))
print 'Calculating MAE for algorithm...'
algorithm_MAE.append(calMAE(ranking, str(user)))
print original_MAE
print algorithm_MAE
# training[str(key + 1)] = original_prefs[str(key+1)]
cid = 'two'
for user in validation:
user_dict, removed_items = getRemovedPrefs(validation[user], 10)
org_prefs = {}
org_prefs = validation
org_prefs[user] = user_dict
print "Length of org_prefs %d" % (len(org_prefs))
#use the original prefs here.
print 'Calculating original item similarity...'
oitemsim = pickle.load(
open('../pickle/original/oitemsim_' + cid + '.pickle', "rb"))
#oitemsim = original.calculateSimilarItems(org_prefs, n=50)
print 'Calculating original ranking...'
oranking = original.getRecommendedItems(org_prefs, oitemsim, user)
print 'Calculating MAE for original...'
original_MAE.append(getMAE(oranking, user, 'org'))
#print original_MAE
#print algorithm_MAE
print sum(original_MAE) / len(original_MAE)
pickle.dump(orgdetails,
open("../rating_details/" + cid + "/orgdetails_" + cid, "wb"))
istart = time.time()
itemsim = algorithm.calculateSimilarItems(clusterPrefs, n=50)
istop = time.time()
for user in clusterPrefs:
#fix missing values
if not user:
continue
print "Cluster: %d User: %s" % (cid, user)
rstart = time.time()
#ranking = algorithm.getRecommendedItems(testingPrefs, clusterPrefs,itemsim,user)
ranking = original.getRecommendedItems(testingPrefs, itemsim, user)
rstop = time.time()
orstart = time.time()
oranking = original.getRecommendedItems(testingPrefs, oitemsim,
user)
orstop = time.time()
print '**************STATS****************'
print 'algo item sim: %d s' % (abs(istart - istop))
print 'orig item sim: %d s' % (abs(ostart - ostop))
print 'algo reco time: %d s' % (abs(rstart - rstop))
print 'orig reco time: %d s' % (abs(orstart - orstop))
print '**************END STATS************'
original_MAE.append(getMAE(oranking, user))
cid = 'two'
for user in validation:
user_dict, removed_items = getRemovedPrefs(validation[user], 10)
org_prefs = {}
org_prefs = validation
org_prefs[user] = user_dict
print "Length of org_prefs %d"%(len(org_prefs))
#use the original prefs here.
print 'Calculating original item similarity...'
oitemsim = pickle.load( open( '../pickle/original/oitemsim_' + cid + '.pickle', "rb" ))
#oitemsim = original.calculateSimilarItems(org_prefs, n=50)
print 'Calculating original ranking...'
oranking = original.getRecommendedItems(org_prefs, oitemsim,user)
print 'Calculating MAE for original...'
original_MAE.append(getMAE(oranking,user, 'org'))
#print original_MAE
#print algorithm_MAE
print sum(original_MAE)/len(original_MAE)
pickle.dump(orgdetails, open("../rating_details/" + cid +"/orgdetails_" + cid, "wb"))