import os
import sys
sys.path.append(os.getcwd())
import recommendation
print recommendation.rmse('result')
rmse = 0.0 test_records = 0 counter = 0 for user in customer: counter += 1 recommendation.init_item_warehouse(item_warehouse) for item in item_warehouse: if item not in customer[user]['items']: score = vector.dot(customer[user]['feature'],item_warehouse[item]['feature']) if score > 5: score = 5. item_warehouse[item]['score'] = score if user in tester: result = recommendation.rmse(tester[user],item_warehouse) rmse += result[0] test_records += result[1] # print rmse,test_records rmse_local += sqrt(rmse * 1.0 / test_records) print rmse_local / 5. ''' if user in tester: test_records += len(tester[user]) for titem in tester[user]: rs = recommendation.rankingscore(customer[user],titem,item_warehouse) ranking_score += rs
movies = ['mv_0013581','mv_0011328','mv_0008433','mv_0004607','mv_0000001',
'mv_0000408']
# Predict the ratings corresponding to (movie,user) pair in test files in test
# folder
result = open('result','w')
for movie in movies:
f = open('test/'+movie+'.txt')
lines = f.readlines()
f.close()
user_list = []
for i in range(1,len(lines)):
line = lines[i]
u = line.split(',',1)[0]
user_list.append(u)
for user in user_list:
start = time.clock()
test = recommendation.getRate(user,movie)
print movie+'\t'+user+'\t'+str(test)
print 'Prediction time: '+str(time.clock()-start)+' seconds\n'
result.write(movie+'\t'+user+'\t'+str(test)+'\t'+str(time.clock()-start)
+'\n')
result.close()
# Evaluate accuracy
accuracy = recommendation.rmse('result')
print 'RMSE: '+str(accuracy)
prepare.create_error_statistic_file('result')