Package: scikit-surprise
OverviewSurpriseis a Pythonscikitfor building and analyzing
recommender systems that deal with explicit rating data.Surprisewas designed with the
following purposes in mind:Give users perfect control over their experiments. To this end, a strong
emphasis is laid ondocumentation, which we
have tried to make as clear and precise as possible by pointing out every
detail of the algorithms.Alleviate the pain ofDataset
handling.
Users can use bothbuilt-indatasets
(Movielens,Jester), and their owncustomdatasets.Provide various ready-to-useprediction
algorithmssuch asbaseline
algorithms,neighborhood
methods, matrix
factorization-based (SVD,PMF,SVD++,NMF),
andmany
others.
Also, varioussimilarity
measures(cosine, MSD, pearson...) are built-in.Make it easy to implementnew algorithm
ideas.Provide tools toevaluate,analyseandcomparethe algorithms' performance. Cross-validation procedures can be run very
easily using powerful CV iterators (inspired byscikit-learnexcellent tools), as well asexhaustive search over a set of
parameters.The nameSurPRISE(roughly :) ) stands forSimple Python RecommendatIon
System Engine.Please note that surprise does not support implicit ratings or content-based
information.Getting started, exampleHere is a simple example showing how you can (down)load a dataset, split it for
5-fold cross-validation, and compute the MAE and RMSE of theSVDalgorithm.fromsurpriseimportSVDfromsurpriseimportDatasetfromsurprise.model_selectionimportcross_validate# Load the movielens-100k dataset (download it if needed).data=Dataset.load_builtin('ml-100k')# Use the famous SVD algorithm.algo=SVD()# Run 5-fold cross-validation and print results.cross_validate(algo,data,measures=['RMSE','MAE'],cv=5,verbose=True)Output:Evaluating RMSE, MAE of algorithm SVD on 5 split(s).

                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     
RMSE (testset)    0.9367  0.9355  0.9378  0.9377  0.9300  0.9355  0.0029  
MAE (testset)     0.7387  0.7371  0.7393  0.7397  0.7325  0.7375  0.0026  
Fit time          0.62    0.63    0.63    0.65    0.63    0.63    0.01    
Test time         0.11    0.11    0.14    0.14    0.14    0.13    0.02Surprisecan domuchmore (e.g,GridSearchCV)!
You'll findmore usage
examplesin thedocumentation.BenchmarksHere are the average RMSE, MAE and total execution time of various algorithms
(with their default parameters) on a 5-fold cross-validation procedure. The
datasets are theMovielens100k and
1M datasets. The folds are the same for all the algorithms. All experiments are
run on a laptop with an intel i5 11th Gen 2.60GHz. The code
for generating these tables can be found in thebenchmark
example.Movielens 100kRMSEMAETimeSVD0.9340.7370:00:06SVD++ (cache_ratings=False)0.9190.7210:01:39SVD++ (cache_ratings=True)0.9190.7210:01:22NMF0.9630.7580:00:06Slope One0.9460.7430:00:09k-NN0.980.7740:00:08Centered k-NN0.9510.7490:00:09k-NN Baseline0.9310.7330:00:13Co-Clustering0.9630.7530:00:06Baseline0.9440.7480:00:02Random1.5181.2190:00:01Movielens 1MRMSEMAETimeSVD0.8730.6860:01:07SVD++ (cache_ratings=False)0.8620.6720:41:06SVD++ (cache_ratings=True)0.8620.6720:34:55NMF0.9160.7230:01:39Slope One0.9070.7150:02:31k-NN0.9230.7270:05:27Centered k-NN0.9290.7380:05:43k-NN Baseline0.8950.7060:05:55Co-Clustering0.9150.7170:00:31Baseline0.9090.7190:00:19Random1.5041.2060:00:19InstallationWith pip (you'll neednumpy, and a C compiler. Windows
users might prefer using conda):$ pip install numpy
$ pip install scikit-surpriseWith conda:$ conda install -c conda-forge scikit-surpriseFor the latest version, you can also clone the repo and build the source
(you'll first needCythonandnumpy):$ pip install numpy cython
$ git clone https://github.com/NicolasHug/surprise.git
$ cd surprise
$ python setup.py installLicense and referenceThis project is licensed under theBSD
3-Clauselicense, so it can be
used for pretty much everything, including commercial applications.I'd love to know how Surprise is useful to you. Please don't hesitate to open
an issue and describe how you use it!Please make sure to cite thepaperif you use
Surprise for your research:@article{Hug2020,
  doi = {10.21105/joss.02174},
  url = {https://doi.org/10.21105/joss.02174},
  year = {2020},
  publisher = {The Open Journal},
  volume = {5},
  number = {52},
  pages = {2174},
  author = {Nicolas Hug},
  title = {Surprise: A Python library for recommender systems},
  journal = {Journal of Open Source Software}
}ContributorsThe following persons have contributed toSurprise:ashtou, bobbyinfj, caoyi, Олег Демиденко, Charles-Emmanuel Dias, dmamylin,
Lauriane Ducasse, Marc Feger, franckjay, Lukas Galke, Tim Gates, Pierre-François
Gimenez, Zachary Glassman, Jeff Hale, Nicolas Hug, Janniks, jyesawtellrickson,
Doruk Kilitcioglu, Ravi Raju Krishna, lapidshay, Hengji Liu, Ravi Makhija, Maher
Malaeb, Manoj K, James McNeilis, Naturale0, nju-luke, Pierre-Louis Pécheux, Jay
Qi, Lucas Rebscher, Skywhat, Hercules Smith, David Stevens, Vesna Tanko,
TrWestdoor, Victor Wang, Mike Lee Williams, Jay Wong, Chenchen Xu, YaoZh1918.Thanks a lot :) !Development StatusStarting from version 1.1.0 (September 2019), I will only maintain the package,
provide bugfixes, and perhaps sometimes perf improvements. I have less time to
dedicate to it now, so I'm unabe to consider new features.For bugs, issues or questions aboutSurprise, please
avoid sending me emails; I will most likely not be able to answer). Please use
the GitHubproject pageinstead, so
that others can also benefit from it.