条件:ML100k.data
注意 :程序对列表进行修改,为避免列表索引出现问题,避免使用for i in range(len(data)),而使用 for i in data可避免这一问题
python
import pickle
data = []
with open("ML100k.data", 'r') as file:
for line in file:
data.append([int(item) for item in line.strip('\n').split('\t')])
user_dict = {}
film_dict = {}
for i in data:
user = i[0]
film = i[1]
if user not in user_dict:
user_dict[user] = [i]
else:
user_dict[user].append(i)
if film not in film_dict:
film_dict[film] = [i]
else:
film_dict[film].append(i)
# 删掉不活跃用户、冷门电影
for user in user_dict:
if len(user_dict[user]) < 5:
for _ in data:
if _ in user_dict[user]:
data.remove(_)
for film in film_dict:
if len(film_dict[film]) < 5:
for _ in data:
if _ in film_dict[film]:
data.remove(_)
# 统计用户数量user_num、电影数量item_num、评分数量rating_num
user_sum = {}
item_sum = {}
for i in data:
user = i[0]
item = i[1]
if user not in user_sum:
user_sum[user] = [i]
else:
user_sum[user].append(i)
if item not in item_sum:
item_sum[item] = [i]
else:
item_sum[item].append(i)
print(len(user_sum))
print(len(item_sum))
print(len(data))
# 计算稀疏度
sparsity = len(data)/(len(user_sum)*len(item_sum))
print(sparsity)
# 统计每个用户的平均评分user_average、每部电影的平均评分item_average、以及全部评分的平均评分global_average.
user_average = []
item_average = []
sorted_user = list(user_sum.keys())
sorted_item = list(item_sum.keys())
sorted_item.sort()
sorted_user.sort()
for user in sorted_user:
user_average.append(sum(user_sum[user][2])/len(user_sum[user]))
for item in sorted_item:
item_average.append(sum(item_sum[item][2])/len(item_sum[item]))
# print(user_average)
# print(item_average)
# 统计所有评分中1~5的分布情况rating_num
rating_num = [0, 0, 0, 0, 0]
for i in data:
rating = i[2]
rating_num[rating-1] += 1
print(rating_num)
# 将用户和电影分别从0开始标号,使得用户的最大编号为user_sum-1,电影最大编号为item_sum-1
user_num = {}
item_num = {}
count1, count2 = 0, 0
for i in data:
user = i[0]
item = i[1]
if user not in user_num:
user_num[user] = count1
count1 += 1
if item not in item_num:
item_num[item] = count2
count2 += 1
i[0] = user_num[user]
i[1] = item_num[item]