Package: similaripy
SimilariPyThis project provides fast Python implementation of several KNN (K-Nearest Neighbors) similarity algorithms using sparse matrices, useful in Collaborative Filtering Recommender Systems and others.The package also include some normalization functions that could be useful in the pre-processing phase before the similarity computation.SimilaritiesBase similarity models:Dot ProductCosine and Asymmetric CosineTversky, Jaccard and DiceGraph-based similarity models:P3α and RP3βAdvanced similarity model:S-PlusFor additional information see thesimilarities documentation.All the models have multi-threaded routines, using Cython and OpenMP to fit the models in parallel among all available CPU cores.NormalizationsThe package contains normalization functions like: l1, l2, max, tf-idf, bm25, bm25+.For tf-idf, bm25, bm25+ you could chose the log-base and how the term-frequency (TF) and the inverse document frequency (IDF) are computed.All the functions are compiled at low-level and could operate in-place on csr-matrixes, if you need to save memory.Usageimportsimilaripyassimimportscipy.sparseassps# create a random user-rating matrix (URM)urm=sps.random(1000,2000,density=0.025)# normalize matrix with bm25urm=sim.normalization.bm25(urm)# train the model with 50 knn per itemmodel=sim.cosine(urm.T,k=50)# recommend 100 items to users 1, 14 and 8 filtering the items already seen by each usersuser_recommendations=sim.dot_product(urm,model.T,k=100,target_rows=[1,14,8],filter_cols=urm)InstallationSimilaripy can be installed from PyPI with:pip install similaripyRequirementsPackageVersionnumpy>= 1.14scipy>= 1.0.0tqdm>= 4.19.6cython>= 0.28.1GCC compilerIn order to compile the Cython code it is required a GCC compiler with OpenMP.To install it you can follow the next steps according to your OS.Ubuntu / DebianIt can be installed with the dev-tools:sudo apt update && sudo apt install build-essentialMacOSIt can be installed with homebrew:brew install gccNOTE: the package works on both Intel and Apple Silicon chips.WindowsIt can be installed with the Visual C++ Build Tools.They can be downloaded directly from Microsoft:https://visualstudio.microsoft.com/en/visual-cpp-build-tools/.NOTE: on Windows there are issues with the flagformat_output='csr', just leave it set to the default value'coo'.Optimal Configuration for Intel CPUsI recommend configuring SciPy/Numpy to use Intel's MKL matrix libraries.
The easiest way of doing this is by installing the Anaconda Python distribution.HistoryThe idea of build this library comes from theRecSys Challenge 2018organized by Spotify.My team, the Creamy Fireflies, had problems in computing huge similarity models in a reasonable time (66 million interactions in the user-rating matrix), and the use of Python and Numpy was not suitable as a full day was required to compute one single model.As a member of the team, I have spent a lot of time developing these high-performance similarities in Cython to overcome this issue. At the end of the competition, pushed by my teammates, I decided to release my job to help people who will one day face the same problem.Thanks to my Creamy Fireflies friends for supporting me.LicenseReleased under the MIT LicenseCitation information:@misc{boglio_simone_similaripy,
  author       = {Boglio Simone},
  title        = {bogliosimone/similaripy},
  doi          = {10.5281/zenodo.2583851},
  url          = {https://doi.org/10.5281/zenodo.2583851}
}