# Surprise **Repository Path**: personalDemo/Surprise ## Basic Information - **Project Name**: Surprise - **Description**: A Python scikit for building and analyzing recommender systems - **Primary Language**: Python - **License**: BSD-3-Clause - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-02-20 - **Last Updated**: 2021-06-21 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README [![GitHub version](https://badge.fury.io/gh/nicolashug%2FSurprise.svg)](https://badge.fury.io/gh/nicolashug%2FSurprise) [![Documentation Status](https://readthedocs.org/projects/surprise/badge/?version=stable)](http://surprise.readthedocs.io/en/stable/?badge=stable) [![Build Status](https://travis-ci.org/NicolasHug/Surprise.svg?branch=master)](https://travis-ci.org/NicolasHug/Surprise) [![python versions](https://img.shields.io/badge/python-2.7%2C%203.5%2C%203.6-blue.svg)](http://surpriselib.com) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) Surprise ======== Overview -------- [Surprise](http://surpriselib.com) is a Python [scikit](https://www.scipy.org/scikits.html) building and analyzing recommender systems. [Surprise](http://surpriselib.com) **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](http://surprise.readthedocs.io/en/stable/index.html), 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](http://surprise.readthedocs.io/en/stable/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/stable/prediction_algorithms_package.html) such as [baseline algorithms](http://surprise.readthedocs.io/en/stable/basic_algorithms.html), [neighborhood methods](http://surprise.readthedocs.io/en/stable/knn_inspired.html), matrix factorization-based ( [SVD](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD), [PMF](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#unbiased-note), [SVD++](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp), [NMF](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.NMF)), and [many others](http://surprise.readthedocs.io/en/stable/prediction_algorithms_package.html). Also, various [similarity measures](http://surprise.readthedocs.io/en/stable/similarities.html) (cosine, MSD, pearson...) are built-in. - Make it easy to implement [new algorithm ideas](http://surprise.readthedocs.io/en/stable/building_custom_algo.html). - Provide tools to [evaluate](http://surprise.readthedocs.io/en/stable/model_selection.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 using powerful CV iterators (inspired by [scikit-learn](http://scikit-learn.org/) excellent tools), as well as [exhaustive search over a set of parameters](http://surprise.readthedocs.io/en/stable/getting_started.html#tune-algorithm-parameters-with-gridsearchcv). The name *SurPRISE* (roughly :) ) stands for Simple Python RecommendatIon System Engine. 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](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD) algorithm. ```python 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](http://surpriselib.com) can do **much** more (e.g, [GridSearchCV](http://surprise.readthedocs.io/en/stable/getting_started.html#tune-algorithm-parameters-with-gridsearchcv))! You'll find [more usage examples](http://surprise.readthedocs.io/en/stable/getting_started.html) in the [documentation ](http://surprise.readthedocs.io/en/stable/index.html). 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](http://grouplens.org/datasets/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](https://github.com/NicolasHug/Surprise/tree/master/examples/benchmark.py). | [Movielens 100k](http://grouplens.org/datasets/movielens/100k) | RMSE | MAE | Time | |:---------------------------------------------------------------------------------------------------------------------------------------|-------:|------:|:--------| | [SVD](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD) | 0.934 | 0.737 | 0:00:11 | | [SVD++](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp) | 0.92 | 0.722 | 0:09:03 | | [NMF](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.NMF) | 0.963 | 0.758 | 0:00:15 | | [Slope One](http://surprise.readthedocs.io/en/stable/slope_one.html#surprise.prediction_algorithms.slope_one.SlopeOne) | 0.946 | 0.743 | 0:00:08 | | [k-NN](http://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBasic) | 0.98 | 0.774 | 0:00:10 | | [Centered k-NN](http://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNWithMeans) | 0.951 | 0.749 | 0:00:10 | | [k-NN Baseline](http://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBaseline) | 0.931 | 0.733 | 0:00:12 | | [Co-Clustering](http://surprise.readthedocs.io/en/stable/co_clustering.html#surprise.prediction_algorithms.co_clustering.CoClustering) | 0.963 | 0.753 | 0:00:03 | | [Baseline](http://surprise.readthedocs.io/en/stable/basic_algorithms.html#surprise.prediction_algorithms.baseline_only.BaselineOnly) | 0.944 | 0.748 | 0:00:01 | | [Random](http://surprise.readthedocs.io/en/stable/basic_algorithms.html#surprise.prediction_algorithms.random_pred.NormalPredictor) | 1.514 | 1.215 | 0:00:01 | | [Movielens 1M](http://grouplens.org/datasets/movielens/1m) | RMSE | MAE | Time | |:---------------------------------------------------------------------------------------------------------------------------------------|-------:|------:|:--------| | [SVD](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD) | 0.873 | 0.686 | 0:02:13 | | [SVD++](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp) | 0.862 | 0.673 | 2:54:19 | | [NMF](http://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.NMF) | 0.916 | 0.724 | 0:02:31 | | [Slope One](http://surprise.readthedocs.io/en/stable/slope_one.html#surprise.prediction_algorithms.slope_one.SlopeOne) | 0.907 | 0.715 | 0:02:31 | | [k-NN](http://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBasic) | 0.923 | 0.727 | 0:05:27 | | [Centered k-NN](http://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNWithMeans) | 0.929 | 0.738 | 0:05:43 | | [k-NN Baseline](http://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBaseline) | 0.895 | 0.706 | 0:05:55 | | [Co-Clustering](http://surprise.readthedocs.io/en/stable/co_clustering.html#surprise.prediction_algorithms.co_clustering.CoClustering) | 0.915 | 0.717 | 0:00:31 | | [Baseline](http://surprise.readthedocs.io/en/stable/basic_algorithms.html#surprise.prediction_algorithms.baseline_only.BaselineOnly) | 0.909 | 0.719 | 0:00:19 | | [Random](http://surprise.readthedocs.io/en/stable/basic_algorithms.html#surprise.prediction_algorithms.random_pred.NormalPredictor) | 1.504 | 1.206 | 0:00:19 | Installation ------------ With pip (you'll need [numpy](http://www.numpy.org/), 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](http://cython.org/) and [numpy](http://www.numpy.org/)): $ pip install numpy cython $ git clone https://github.com/NicolasHug/surprise.git $ cd surprise $ python setup.py install License ------- This project is licensed under the [BSD 3-Clause](https://opensource.org/licenses/BSD-3-Clause) license, so it can be used for pretty much everything, including commercial applications. Please let us know how [Surprise](http://surpriselib.com) 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} } Contributors ------------ The following persons have contributed to [Surprise](http://surpriselib.com): Олег Демиденко, Charles-Emmanuel Dias, Lukas Galke, Pierre-François Gimenez, Nicolas Hug, Hengji Liu, Maher Malaeb, Naturale0, nju-luke, Skywhat, David Stevens, Mike Lee Williams, Chenchen Xu. Thanks a lot :) ! Contributing, feedback, contact ------------------------------- 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! Please feel free to contribute (see [guidelines](https://github.com/NicolasHug/Surprise/blob/master/.github/CONTRIBUTING.md)) and send pull requests! For bugs, issues or questions about [Surprise](http://surpriselib.com), you can use the GitHub [project page](https://github.com/NicolasHug/Surprise) (please don't send me emails as there would be no record for other users).