Surprise is a Python scikit building and analyzing recommender systems.
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.
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 SVD
from surprise import Dataset
from 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
RMSE 0.9311 0.9370 0.9320 0.9317 0.9391 0.9342 0.0032
MAE 0.7350 0.7375 0.7341 0.7342 0.7375 0.7357 0.0015
Fit time 6.53 7.11 7.23 7.15 3.99 6.40 1.23
Test time 0.26 0.26 0.25 0.15 0.13 0.21 0.06
Surprise can do much more (e.g, GridSearchCV)! You'll find more usage examples in the documentation .
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.
| Movielens 100k | RMSE | MAE | Time |
|---|---|---|---|
| SVD | 0.934 | 0.737 | 0:00:11 |
| SVD++ | 0.92 | 0.722 | 0:09:03 |
| NMF | 0.963 | 0.758 | 0:00:15 |
| Slope One | 0.946 | 0.743 | 0:00:08 |
| k-NN | 0.98 | 0.774 | 0:00:10 |
| Centered k-NN | 0.951 | 0.749 | 0:00:10 |
| k-NN Baseline | 0.931 | 0.733 | 0:00:12 |
| Co-Clustering | 0.963 | 0.753 | 0:00:03 |
| Baseline | 0.944 | 0.748 | 0:00:01 |
| Random | 1.514 | 1.215 | 0:00:01 |
| Movielens 1M | RMSE | MAE | Time |
|---|---|---|---|
| SVD | 0.873 | 0.686 | 0:02:13 |
| SVD++ | 0.862 | 0.673 | 2:54:19 |
| NMF | 0.916 | 0.724 | 0:02:31 |
| Slope One | 0.907 | 0.715 | 0:02:31 |
| k-NN | 0.923 | 0.727 | 0:05:27 |
| Centered k-NN | 0.929 | 0.738 | 0:05:43 |
| k-NN Baseline | 0.895 | 0.706 | 0:05:55 |
| Co-Clustering | 0.915 | 0.717 | 0:00:31 |
| Baseline | 0.909 | 0.719 | 0:00:19 |
| Random | 1.504 | 1.206 | 0:00:19 |
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
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!
Here is a Bibtex entry if you ever need to cite Surprise in a research paper (please keep us posted, we would love to know if Surprise was helpful to you):
@Misc{Surprise,
author = {Hug, Nicolas},
title = { {S}urprise, a {P}ython library for recommender systems},
howpublished = {\url{http://surpriselib.com}},
year = {2017}
}
The following persons have contributed to Surprise:
caoyi, Олег Демиденко, Charles-Emmanuel Dias, dmamylin, Lauriane Ducasse, franckjay, Lukas Galke, Pierre-François Gimenez, Nicolas Hug, Doruk Kilitcioglu, Ravi Raju Krishna, Hengji Liu, Maher Malaeb, Manoj K, Naturale0, nju-luke, Skywhat, David Stevens, Mike Lee Williams, Chenchen Xu, YaoZh1918, Zachary Glassman.
Thanks a lot :) !
Any kind of feedback/criticism would be greatly appreciated (software design, documentation, improvement ideas, spelling mistakes, etc...).
If you'd like to see some features or algorithms implemented in Surprise, please let us know!
Please feel free to contribute (see guidelines) and send pull requests!
For bugs, issues or questions about Surprise, you can use the GitHub project page (please don't send me emails as there would be no record for other users).