A Python scikit for building and analyzing recommender systems

Overview

Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data.

Surprise was designed with the following purposes in mind:

• Give users perfect control over their experiments. To this end, a strong emphasis is laid on documentation, which we have tried to make as clear and precise as possible by pointing out every detail of the algorithms.
• Alleviate the pain of Dataset handling. Users can use both built-in datasets (Movielens, Jester), and their own custom datasets.
• Provide various ready-to-use prediction algorithms such as baseline algorithms, neighborhood methods, matrix factorization-based ( SVD, PMF, SVD++, NMF), and many others. Also, various similarity measures (cosine, MSD, pearson…) are built-in.
• Make it easy to implement new algorithm ideas.
• Provide tools to evaluate, analyse and compare the algorithms’ performance. Cross-validation procedures can be run very easily using powerful CV iterators (inspired by scikit-learn excellent tools), as well as exhaustive search over a set of parameters.

The name SurPRISE (roughly ? ) stands for Simple Python RecommendatIon System Engine.

Please note that surprise does not support implicit ratings or content-based information.

Getting started, example

Here 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 the SVD algorithm.

from surprise import SVDfrom surprise import Datasetfrom surprise.model_selection import cross_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

Surprise can do much more (e.g, GridSearchCV)! You’ll find more usage examples in the documentation .

Benchmarks

Here 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 the Movielens 100k and 1M datasets. The folds are the same for all the algorithms. All experiments are run on a notebook with Intel Core i5 7th gen (2.5 GHz) and 8Go RAM. The code for generating these tables can be found in the benchmark example.

Installation

With pip (you’ll need numpy, and a C compiler. Windows users might prefer using conda):

$ pip install numpy
$ pip install scikit-surprise

With conda:

$ conda install -c conda-forge scikit-surprise

For the latest version, you can also clone the repo and build the source (you’ll first need Cython and numpy):

$ pip install numpy cython
$ git clone https://github.com/NicolasHug/surprise.git
$ cd surprise
$ python setup.py install

License and reference

This project is licensed under the BSD 3-Clause license, so it can be used for pretty much everything, including commercial applications. Please let us know how Surprise is useful to you!

Please make sure to cite the paper if 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}
}

GitHub

https://github.com/NicolasHug/Surprise