Python推荐系统库--Surprise实战

from surprise import KNNBasic, SVD
from surprise import Dataset
from surprise import evaluate, print_perf

# 使用公开的推荐系统数据集--MovieLens
data = Dataset.load_builtin('ml-100k')
# k 折交叉验证
data.split(n_folds=3)
# 算法使用SVD分解
algo = SVD()
# 在数据集上测试效果，算出最小均方根误差、平均绝对误差
perf = evaluate(algo, data, measures=['RMSE', 'MAE'])
# 输出结果
print_perf(perf)

# 算法调参
from surprise import GridSearch
# 迭代轮次、学习率、
# 三个参数，每个有两个参数，2^3 = 8种可能
param_grid = {'n_epochs':[5, 10], 'lr_all':[0.002, 0.005],
'reg_all':[0.4, 0.6]}# 使用SVD算法，三个参数参与调参，评估标准使用最小均方根误差、协调对分数
grid_search = GridSearch(SVD, param_grid, measures=['RMSE', 'FCP'])
data = Dataset.load_builtin('ml-100k')
data.split(n_folds=3)grid_search.evaluate(data)

# 输出最优的参数组
# 输出最好的RMSE结果
print(grid_search.best_score['RMSE'])# 输出对应最好的RMSE结果的参数
print(grid_search.best_params['RMSE'])

# 最好的FCP得分
print(grid_search.best_score['FCP'])# 输出对应最好的FCP结果的参数
print(grid_search.best_params['FCP'])

import os
from surprise import Reader, Dataset
# 指定文件路径
file_path = os.path.expanduser('./popular_music_suprise_format.txt')
# 指定文件格式
reader = Reader(line_format='user item rating timestamp', sep=',')
# 从文件读取数据
music_data = Dataset.load_from_file(file_path, reader=reader)
# 分成5折
music_data.split(n_folds=5)

### 使用NormalPredictor
from surprise import NormalPredictor, evaluate
algo = NormalPredictor()
perf = evaluate(algo, music_data, measures=['RMSE', 'MAE'])### 使用BaselineOnly
from surprise import BaselineOnly, evaluate
algo = BaselineOnly()
perf = evaluate(algo, music_data, measures=['RMSE', 'MAE'])### 使用基础版协同过滤
from surprise import KNNBasic, evaluate
algo = KNNBasic()
perf = evaluate(algo, music_data, measures=['RMSE', 'MAE'])### 使用均值协同过滤
from surprise import KNNWithMeans, evaluate
algo = KNNWithMeans()
perf = evaluate(algo, music_data, measures=['RMSE', 'MAE'])### 使用协同过滤baseline
from surprise import KNNBaseline, evaluate
algo = KNNBaseline()
perf = evaluate(algo, music_data, measures=['RMSE', 'MAE'])### 使用SVD
from surprise import SVD, evaluate
algo = SVD()
perf = evaluate(algo, music_data, measures=['RMSE', 'MAE'])### 使用SVD++
from surprise import SVDpp, evaluate
algo = SVDpp()
perf = evaluate(algo, music_data, measures=['RMSE', 'MAE'])### 使用NMF
from surprise import NMF
algo = NMF()
perf = evaluate(algo, music_data, measures=['RMSE', 'MAE'])
print_perf(perf)

# 在协同过滤算法建模以后，根据item取回相似度最高的item
# 使用的是 algo.get_neighbors()from __future__ import (absolute_import, division, print_function, unicode_literals)
import os
import iofrom surprise import KNNBaseline
from surprise import Dataset

# 获取电影名到电影id 和 电影id到电影名的映射
def read_item_names():
file_name = (os.path.expanduser('~') + '/.surprise_data/ml-100k/ml-100k/u.item')
rid_to_name = {}
name_to_rid = {}
with io.open(file_name, 'r', encoding='ISO-8859-1') as f:
for line in f:
line = line.split('|')
rid_to_name[line[0]] = line[1]
name_to_rid[line[1]] = line[0]
return rid_to_name, name_to_rid# 用算法计算相互间的相似度
data = Dataset.load_builtin('ml-100k')
trainest = data.build_full_trainset()
sim_options = {'name': 'pearson_baseline', 'user_based': False}
algo = KNNBaseline(sim_options=sim_options)
algo.train(trainest)

# 获取电影名到电影id 和 电影id到电影名的映射
rid_to_name, name_to_rid = read_item_names()# 获取玩具总动员的内部id
toy_story_raw_id = name_to_rid['Toy Story (1995)']
toy_story_raw_id

toy_story_inner_id = algo.trainset.to_inner_iid(toy_story_raw_id)
toy_story_inner_id

toy_story_neighbors = algo.get_neighbors(toy_story_inner_id, k=10)
toy_story_neighbors

# 将邻居的内部id转换为名称。
toy_story_neighbors = (algo.trainset.to_raw_iid(inner_id) for inner_id in toy_story_neighbors)toy_story_neighbors = (rid_to_name[rid] for rid in toy_story_neighbors)print()
print('The 10 nearest neighbors of Toy Story are:')
for movie in toy_story_neighbors:
print(movie)