python-recsys 2 Quickstart 2 快速开始

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原文地址：http://ocelma.net/software/python-recsys/build/html/quickstart.html

一旦你已经安装好pyrecsys，你可以：

2.0 设置VERBOSE模式，看到更多的信息：

>>> import recsys.algorithm>>> recsys.algorithm.VERBOSE = True

2.1 下载数据集（首先下载Movielens 1M Ratings Data Set, ratings.dat 文件,下载地址为https://grouplens.org/datasets/movielens/）：

>>> from recsys.algorithm.factorize import SVD>>> svd = SVD()>>> svd.load_data(filename='./data/movielens/ratings.dat', sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})Loading ./data/movielens/ratings.dat..........|

2.2 计算SVD，M=U

>>> k = 100>>> svd.compute(k=k, min_values=10, pre_normalize=None, mean_center=True, post_normalize=True)Creating matrix (1000209 tuples)Matrix density is: 4.4684%Updating matrix: squish to at least 10 valuesComputing svd k=100, min_values=10, pre_normalize=None, mean_center=True, post_normalize=True

你也可以将输出的SVD模型保存起来（以zip的文件格式保存）

>>> k = 100>>> svd.compute(k=k, min_values=10, pre_normalize=None, mean_center=True, post_normalize=True, savefile='/tmp/movielens')Creating matrix (1000209 tuples)Matrix density is: 4.4684%Updating matrix: squish to at least 10 valuesComputing svd k=100, min_values=10, pre_normalize=None, mean_center=True, post_normalize=TrueSaving svd model to /tmp/movielens

Note

For more information about svd.compute() parameters see Algorithms section.

注意：为了得到更多关于 svd.compute() 参数的信息，可以看 Algorithms 这一部分。

一旦这个SVD模型已经保存起来（zip格式）你可以随时使用它，而且不需要再一次的去计算svd.compute()。

2.3 计算两个电影之间的相似度

>>> ITEMID1 = 1    # Toy Story (1995)>>> ITEMID2 = 2355 # A bug's life (1998)>>> svd.similarity(ITEMID1, ITEMID2)0.67706936677315799

2.4 获得类似于Toy Story（玩具总动员）的电影：

>>> svd.similar(ITEMID1)[(1,    0.99999999999999978), # Toy Story (3114, 0.87060391051018071), # Toy Story 2 (2355, 0.67706936677315799), # A bug's life (588,  0.5807351496754426),  # Aladdin (595,  0.46031829709743477), # Beauty and the Beast (1907, 0.44589398718134365), # Mulan (364,  0.42908159895574161), # The Lion King (2081, 0.42566581277820803), # The Little Mermaid (3396, 0.42474056361935913), # The Muppet Movie (2761, 0.40439361857585354)] # The Iron Giant

2.5 对于一个给定的用户和电影进行预测评级

>>> MIN_RATING = 0.0>>> MAX_RATING = 5.0>>> ITEMID = 1>>> USERID = 1>>> svd.predict(ITEMID, USERID, MIN_RATING, MAX_RATING)5.0 #Predicted value>>> svd.get_matrix().value(ITEMID, USERID)5.0 #Real value

2.6 向用户推荐电影

>>> svd.recommend(USERID, is_row=False) #cols are users and rows are items, thus we set is_row=False[(2905, 5.2133848204673416), # Shaggy D.A., The (318,  5.2052108435956033), # Shawshank Redemption, The (2019, 5.1037438278755474), # Seven Samurai (The Magnificent Seven) (1178, 5.0962756861447023), # Paths of Glory (1957) (904,  5.0771405690055724), # Rear Window (1954) (1250, 5.0744156653222436), # Bridge on the River Kwai, The (858,  5.0650911066862907), # Godfather, The (922,  5.0605327279819408), # Sunset Blvd. (1198, 5.0554543765500419), # Raiders of the Lost Ark (1148, 5.0548789542105332)] # Wrong Trousers, The

2.7 哪些用户可以看玩具总动员

>>> svd.recommend(ITEMID)[(283,  5.716264440514446), (3604, 5.6471765418323141), (5056, 5.6218800339214496), (446,  5.5707524860615738), (3902, 5.5494529168484652), (4634, 5.51643364021289), (3324, 5.5138903299082802), (4801, 5.4947999354188548), (1131, 5.4941438045650068), (2339, 5.4916048051511659)]

2.8 对于更大的数据集（超过10M 元组），直接使用SVDLIBC会更好。（divisi2也使用SVDLIBC，但是在创建矩阵和计算SVD时太慢了）

>>> from recsys.utils.svdlibc import SVDLIBC>>> svdlibc = SVDLIBC('./data/movielens/ratings.dat')>>> svdlibc.to_sparse_matrix(sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})>>> svdlibc.compute(k=100)>>> svd = svdlibc.export()>>> svd.similar(ITEMID1) # results might be different than example 4. as there's no min_values=10 set here[(1, 0.99999999999999978), (3114, 0.84099896392054219), (588, 0.79191433686817747), (2355, 0.7772760704844065), (1265, 0.74946256379033827), (364, 0.73730970556786068), (2321, 0.73652131961235268), (595, 0.71665833726881523), (3253, 0.7075696829413568), (1923, 0.69687698887991523)]

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