scikit-surprise 1.0.1

An easy-to-use library for recommender systems.

[](https://badge.fury.io/gh/nicolashug%2FSurprise)
[](http://surprise.readthedocs.io/en/latest/?badge=latest)
[](https://travis-ci.org/NicolasHug/Surprise)
[](http://surpriselib.com)
[](https://opensource.org/licenses/BSD-3-Clause)




Surprise
========

Overview
--------

[Surprise](http://surpriselib.com) is an easy-to-use Python
[scikit](https://www.scipy.org/scikits.html) for recommender systems.

[Surprise](http://surpriselib.com) **was designed with the
following purposes in mind**:

- Give the user perfect control over his experiments. To this end, a strong
emphasis is laid on
[documentation](http://surprise.readthedocs.io/en/latest/index.html), which we
have tried to make as clear and precise as possible by pointing out every
details of the algorithms.
- Alleviate the pain of [Dataset
handling](http://surprise.readthedocs.io/en/latest/getting_started.html#load-a-custom-dataset).
Users can use both *built-in* datasets
([Movielens](http://grouplens.org/datasets/movielens/),
[Jester](http://eigentaste.berkeley.edu/dataset/)), and their own *custom*
datasets.
- Provide various ready-to-use [prediction
algorithms](http://surprise.readthedocs.io/en/latest/prediction_algorithms_package.html)
such as [baseline
algorithms](http://surprise.readthedocs.io/en/latest/basic_algorithms.html),
[neighborhood
methods](http://surprise.readthedocs.io/en/latest/knn_inspired.html), matrix
factorization-based (
[SVD](http://surprise.readthedocs.io/en/latest/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD),
[PMF](http://surprise.readthedocs.io/en/latest/matrix_factorization.html#unbiased-note),
[SVD++](http://surprise.readthedocs.io/en/latest/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp),
[NMF](http://surprise.readthedocs.io/en/latest/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.NMF)),
and [many
others](http://surprise.readthedocs.io/en/latest/prediction_algorithms_package.html).
Also, various [similarity
measures](http://surprise.readthedocs.io/en/latest/similarities.html)
(cosine, MSD, pearson...) are built-in.
- Make it easy to implement [new algorithm
ideas](http://surprise.readthedocs.io/en/latest/building_custom_algo.html).
- Provide tools to [evaluate](http://surprise.readthedocs.io/en/latest/evaluate.html),
[analyse](http://nbviewer.jupyter.org/github/NicolasHug/Surprise/tree/master/examples/notebooks/KNNBasic_analysis.ipynb/)
and
[compare](http://nbviewer.jupyter.org/github/NicolasHug/Surprise/blob/master/examples/notebooks/Compare.ipynb)
the algorithms performance. Cross-validation procedures can be run very
easily, as well as [exhaustive search over a set of
parameters](http://surprise.readthedocs.io/en/latest/getting_started.html#tune-algorithm-parameters-with-gridsearch).


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


Example
-------

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](http://surprise.readthedocs.io/en/latest/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD)
algorithm.

```python
from surprise import SVD
from surprise import Dataset
from surprise import evaluate


# 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)
```

**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](http://surpriselib.com) can do **much** more (e.g,
[GridSearch](http://surprise.readthedocs.io/en/latest/getting_started.html#tune-algorithm-parameters-with-gridsearch)).
Check the [User
Guide](http://surprise.readthedocs.io/en/latest/getting_started.html)!


Benchmarks
----------

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](http://grouplens.org/datasets/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](http://man7.org/linux/man-pages/man1/time.1.html) command.

| [Movielens 100k](http://grouplens.org/datasets/movielens/100k) | RMSE | MAE | Time (s) |
|-----------------|:------:|:------:|:--------:|
| [NormalPredictor](http://surprise.readthedocs.io/en/latest/basic_algorithms.html#surprise.prediction_algorithms.random_pred.NormalPredictor) | 1.5228 | 1.2242 | 4 |
| [BaselineOnly](http://surprise.readthedocs.io/en/latest/basic_algorithms.html#surprise.prediction_algorithms.baseline_only.BaselineOnly) | .9445 | .7488 | 5 |
| [KNNBasic](http://surprise.readthedocs.io/en/latest/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBasic) | .9789 | .7732 | 27 |
| [KNNWithMeans](http://surprise.readthedocs.io/en/latest/knn_inspired.html#surprise.prediction_algorithms.knns.KNNWithMeans) | .9514 | .7500 | 30 |
| [KNNBaseline](http://surprise.readthedocs.io/en/latest/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBaseline) | .9306 | .7334 | 44 |
| [SVD](http://surprise.readthedocs.io/en/latest/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD) | .9396 | .7412 | 46 |
| [SVD++](http://surprise.readthedocs.io/en/latest/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp) | .9200 | .7253 | 31min |
| [NMF](http://surprise.readthedocs.io/en/latest/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.NMF) | .9634 | .7572 | 55 |
| [Slope One](http://surprise.readthedocs.io/en/latest/slope_one.html#surprise.prediction_algorithms.slope_one.SlopeOne) | .9454 | .7430 | 25 |
| [Co clustering](http://surprise.readthedocs.io/en/latest/co_clustering.html#surprise.prediction_algorithms.co_clustering.CoClustering) | .9678 | .7579 | 15 |


| [Movielens 1M](http://grouplens.org/datasets/movielens/1m) | RMSE | MAE | Time (min) |
|-----------------|:------:|:------:|:--------:|
| [NormalPredictor](http://surprise.readthedocs.io/en/latest/basic_algorithms.html#surprise.prediction_algorithms.random_pred.NormalPredictor) | 1.5037 | 1.2051 | < 1 |
| [BaselineOnly](http://surprise.readthedocs.io/en/latest/basic_algorithms.html#surprise.prediction_algorithms.baseline_only.BaselineOnly) | .9086 | .7194 | < 1 |
| [KNNBasic](http://surprise.readthedocs.io/en/latest/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBasic) | .9207 | .7250 | 22 |
| [KNNWithMeans](http://surprise.readthedocs.io/en/latest/knn_inspired.html#surprise.prediction_algorithms.knns.KNNWithMeans) | .9292 | .7386 | 22 |
| [KNNBaseline](http://surprise.readthedocs.io/en/latest/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBaseline) | .8949 | .7063 | 44 |
| [SVD](http://surprise.readthedocs.io/en/latest/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD) | .8936 | .7057 | 7 |
| [NMF](http://surprise.readthedocs.io/en/latest/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.NMF) | .9155 | .7232 | 9 |
| [Slope One](http://surprise.readthedocs.io/en/latest/slope_one.html#surprise.prediction_algorithms.slope_one.SlopeOne) | .9065 | .7144 | 8 |
| [Co clustering](http://surprise.readthedocs.io/en/latest/co_clustering.html#surprise.prediction_algorithms.co_clustering.CoClustering) | .9155 | .7174 | 2 |

Installation / Usage
--------------------

The easiest way is to use pip (you'll need [numpy](http://www.numpy.org/)):

$ pip install scikit-surprise

Or you can clone the repo and build the source (you'll need
[Cython](http://cython.org/) and [numpy](http://www.numpy.org/)):

$ git clone https://github.com/NicolasHug/surprise.git
$ python setup.py install

Documentation, Getting Started
------------------------------

The documentation with many other usage examples is [available
online](http://surprise.readthedocs.io/en/latest/index.html) on ReadTheDocs.

License
-------

This project is licensed under the [BSD
3-Clause](https://opensource.org/licenses/BSD-3-Clause) license.

Acknowledgements:
----------------

- [Pierre-François Gimenez](https://github.com/PFgimenez), for his valuable
insights on software design.
- [Maher Malaeb](https://github.com/mahermalaeb), for the
[GridSearch](http://surprise.readthedocs.io/en/latest/evaluate.html#surprise.evaluate.GridSearch)
implementation.

Contributing, feedback
----------------------

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](http://surpriselib.com), please let us know! Some of the current
ideas are:

- Bayesian PMF
- RBM for CF

Please feel free to contribute (see
[guidelines](https://github.com/NicolasHug/Surprise/blob/master/CONTRIBUTING.md))
and send pull requests!