NFM模型预测报错ZeroDivisionError: float division

问题遇到的现象和发生背景

做音乐推荐时使用NFM模型预测评分报错

import os
import pickle
from surprise import KNNBaseline, Reader
from surprise import Dataset
path = "./popular/"

# 重建歌单id到歌单名的映射字典
id_name_dic = pickle.load(open( path+"popular_playlist.pkl","rb"),encoding='bytes')
print("加载歌单ID到歌单名的映射字典完成...")
# 重建歌单名到歌单id的映射字典
name_id_dic = {}
for playlist_id in id_name_dic:
    name_id_dic[id_name_dic[playlist_id]] = playlist_id
print("加载歌单名到歌单ID的映射字典完成...")

file_path = os.path.expanduser(path+"popular_music_suprise_format.txt")
# 指定文件格式
reader = Reader(line_format='user item rating', sep=',')
# 从文件读取数据
music_data = Dataset.load_from_file(file_path, reader=reader)
# 计算歌曲和歌曲之间的相似度
print("构建数据集...")
trainset = music_data.build_full_trainset()
#sim_options = {'name': 'pearson_baseline', 'user_based': False}

i = list(id_name_dic.keys())[200]
print("歌单名称",id_name_dic[i])
print("歌曲总数：",trainset.n_items)
print("歌单总数：",trainset.n_users)

print("开始训练模型...")
#sim_options = {'user_based': False}
#algo = KNNBaseline(sim_options=sim_options)
algo = KNNBaseline()

algo.fit(trainset)

current_playlist = list(name_id_dic.keys())[10]
print("歌单名称", current_playlist)

# 取出近邻
# 映射名字到id
playlist_id = name_id_dic[current_playlist]
print("歌单ID", playlist_id)
# 取出来对应的内部user id => to_inner_uid
playlist_inner_id = algo.trainset.to_inner_uid(playlist_id)
print("内部ID", playlist_inner_id)

playlist_neighbors = algo.get_neighbors(playlist_inner_id, k=10)

# 把歌曲id转成歌曲名字
# to_raw_uid映射回去
playlist_neighbors = (algo.trainset.to_raw_uid(inner_id)
                       for inner_id in playlist_neighbors)
playlist_neighbors = (id_name_dic[playlist_id]
                       for playlist_id in playlist_neighbors)

print()
print("和歌单 《", current_playlist, "》 最接近的10个歌单为：\n")
for playlist in playlist_neighbors:
    print(playlist,algo.trainset.to_inner_uid(name_id_dic[playlist]))

import pickle
# 重建歌曲id到歌曲名的映射字典
song_id_name_dic = pickle.load(open(path+"popular_song.pkl","rb"))
print("加载歌曲ID到歌曲名的映射字典完成...")
# 重建歌曲名到歌曲id的映射字典
song_name_id_dic = {}
for song_id in song_id_name_dic:
    song_name_id_dic[song_id_name_dic[song_id]] = song_id
print("加载歌曲名到歌曲ID的映射字典完成...")

#内部编码的4号用户
user_inner_id = 7
user_rating = trainset.ur[user_inner_id]
items = map(lambda x:x[0], user_rating)
for song in items:
    print(algo.predict(user_inner_id, song, r_ui=0), song_id_name_dic[algo.trainset.to_raw_iid(song)])

### 使用NMF
from surprise import NMF
from surprise import Dataset

file_path = os.path.expanduser(path+'./popular_music_suprise_format.txt')
# 指定文件格式
reader = Reader(line_format='user item rating', sep=',')
# 从文件读取数据
music_data = Dataset.load_from_file(file_path, reader=reader)
# 构建数据集和建模
algo = NMF()
trainset = music_data.build_full_trainset()
algo.fit(trainset)

user_inner_id = 7
user_rating = trainset.ur[user_inner_id]
items = map(lambda x:x[0], user_rating)
for song in items:
    print(algo.predict(algo.trainset.to_raw_uid(user_inner_id), algo.trainset.to_raw_iid(song), r_ui=0), song_id_name_dic[algo.trainset.to_raw_iid(song)])
运行结果及报错内容

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我的解答思路和尝试过的方法

在网上找到了这个解决方法，但我不会使用，请问怎么做？需要大能解答

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