count_user_item_map.setdefault(cnt, {})
count_user_item_map[cnt].setdefault(user, set())
count_user_item_map[cnt][user].add(item)
# for cnt in count_map:
# print 'the number of behavior which occurs %d is %d' %(cnt, count_map[cnt])
return count_map, count_user_item_map
if __name__ == '__main__':
id_position_map = DataSplit.itemIDMap('../File/tianchi_mobile_recommend_train_item.csv')
test_user_item_map, test_user_item_map_size = DataSplit.userOperate('../File/user_test_data.csv', 4, id_position_map)
fp_behavior_occur_count = open('../File/AnalysisResult/behavior_occur_count.csv', 'w')
total1 = 0
for i in xrange(1, 5):
fp_behavior_occur_count.write('behavior type is %d: \n' %i)
count_map, count_user_item_map = userBuyAppetite('../File/user_train_data.csv', i, id_position_map)
total = 0
for cnt in count_map:
# print count_map[cnt], len(count_user_item_map[cnt])
bingos, test_set_num = DataSplit.computeRatio1(count_user_item_map[cnt], test_user_item_map)
fp_behavior_occur_count.write('behavior occurs = %d, in train data(%d,%d,%.2lf), in test data(%d,%d,%.2lf)\n' \
%(cnt, bingos, count_map[cnt], 100.0 * bingos / count_map[cnt], bingos, test_set_num, 100.0 * bingos / test_set_num))
total += bingos
# total1 += len(count_user_item_map[cnt])
fp_behavior_occur_count.write('%d\n' %total)
print i, total
# fp_behavior_occur_count.write('\n')
# for cnt in count_map:
# fp_behavior_occur_count.write('%d,' %count_map[cnt])
# fp_behavior_occur_count.write('\n')
print total1
if __name__ == '__main__':
id_position_map = DataSplit.itemIDMap(
'../File/tianchi_mobile_recommend_train_item.csv')
test_user_item_map, test_user_item_map_size = DataSplit.userOperate(
'../File/user_test_data.csv', 4, id_position_map)
fp_behavior_occur_count = open(
'../File/AnalysisResult/behavior_occur_count.csv', 'w')
total1 = 0
for i in xrange(1, 5):
fp_behavior_occur_count.write('behavior type is %d: \n' % i)
count_map, count_user_item_map = userBuyAppetite(
'../File/user_train_data.csv', i, id_position_map)
total = 0
for cnt in count_map:
# print count_map[cnt], len(count_user_item_map[cnt])
bingos, test_set_num = DataSplit.computeRatio1(
count_user_item_map[cnt], test_user_item_map)
fp_behavior_occur_count.write('behavior occurs = %d, in train data(%d,%d,%.2lf), in test data(%d,%d,%.2lf)\n' \
%(cnt, bingos, count_map[cnt], 100.0 * bingos / count_map[cnt], bingos, test_set_num, 100.0 * bingos / test_set_num))
total += bingos
# total1 += len(count_user_item_map[cnt])
fp_behavior_occur_count.write('%d\n' % total)
print i, total
# fp_behavior_occur_count.write('\n')
# for cnt in count_map:
# fp_behavior_occur_count.write('%d,' %count_map[cnt])
# fp_behavior_occur_count.write('\n')
print total1
tag = 0
ii = 0
for file_name in os.listdir(rootDir):
file_path = os.path.join(rootDir, file_name)
if tag < cnt:
tag += 1
continue
print '%s proceed begin %d %d %d' %(file_path, ii, tag, cnt)
if ii == 0:
test_user_item_map, test_user_item_map_size = DataSplit.userOperate(file_path, 4, id_position_map, True)
elif ii <= 7:
for i in xrange(1, 5):
everyday_user_item_map, everyday_user_item_map_size = DataSplit.userOperate(file_path, i, id_position_map, True)
print 'behavior %d' %i
# print 'train data: user_item_numbers = %d' %everyday_user_item_map_size
bingos, total = DataSplit.computeRatio1(everyday_user_item_map, test_user_item_map)
# print '(%d,%d,%.2lf)' %(bingos, everyday_user_item_map_size, 100.0 * bingos / everyday_user_item_map_size)
total_user_item_behavior_list[i][ii] += bingos
else:
break
ii += 1
tag += 1
# test_user_item_map, test_user_item_map_size = DataSplit.userOperate('../File/user_test_data.csv', 4, id_position_map)
# print 'test data: user_item_numbers = %d' %test_user_item_map_size
# import os
# rootDir = '../File/EverydayData/'
# total_user_item_behavior_list = []
# for file_name in os.listdir(rootDir):
# user_item_behavior_list = []
# file_path = os.path.join(rootDir, file_name)
# print file_path