The Mangaki recommendation challenge is on! Have fun using Surprise!
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, 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 3-folds cross-validation, and compute the MAE and RMSE of the SVD algorithm.
from surprise import SVD
from surprise import Dataset
from surprise import evaluate, print_perf
# Load the movielens-100k dataset (download it if needed),
# and split it into 3 folds for cross-validation.
data = Dataset.load_builtin('ml-100k')
data.split(n_folds=3)
# We'll use the famous SVD algorithm.
algo = SVD()
# Evaluate performances of our algorithm on the dataset.
perf = evaluate(algo, data, measures=['RMSE', 'MAE'])
print_perf(perf)Output:
Evaluating RMSE, MAE of algorithm SVD.
Fold 1 Fold 2 Fold 3 Mean
MAE 0.7475 0.7447 0.7425 0.7449
RMSE 0.9461 0.9436 0.9425 0.9441
Surprise can do much more (e.g, GridSearch)! 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-folds cross-validation procedure. The datasets are the Movielens 100k and 1M datasets. The folds are the same for all the algorithms (the random seed is set to 0). All experiments are run on a small laptop with Intel Core i3 1.7 GHz, 4Go RAM. The execution time is the real execution time, as returned by the GNU time command.
| Movielens 100k | RMSE | MAE | Time (s) |
|---|---|---|---|
| NormalPredictor | 1.5228 | 1.2242 | 4 |
| BaselineOnly | .9445 | .7488 | 5 |
| KNNBasic | .9789 | .7732 | 27 |
| KNNWithMeans | .9514 | .7500 | 30 |
| KNNBaseline | .9306 | .7334 | 44 |
| SVD | .9364 | .7381 | 46 |
| SVD++ | .9200 | .7253 | 31min |
| NMF | .9634 | .7572 | 55 |
| Slope One | .9454 | .7430 | 25 |
| Co clustering | .9678 | .7579 | 15 |
| Movielens 1M | RMSE | MAE | Time (min) |
|---|---|---|---|
| NormalPredictor | 1.5037 | 1.2051 | < 1 |
| BaselineOnly | .9086 | .7194 | < 1 |
| KNNBasic | .9207 | .7250 | 22 |
| KNNWithMeans | .9292 | .7386 | 22 |
| KNNBaseline | .8949 | .7063 | 44 |
| SVD | .8738 | .6858 | 7 |
| NMF | .9155 | .7232 | 9 |
| Slope One | .9065 | .7144 | 8 |
| Co clustering | .9155 | .7174 | 2 |
The easiest way is to use pip (you'll need numpy):
$ pip install numpy
$ pip install scikit-surprise
Or you can clone the repo and build the source (you'll need Cython and numpy):
$ git clone https://github.com/NicolasHug/surprise.git
$ 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}
}
- Pierre-François Gimenez, for his valuable insights on software design.
- Maher Malaeb, for the GridSearch implementation.
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!
To contact us, send us a tweet or mail us at contact at nicolas-hug dot com. For any bug or issue, please use the GitHub project page.