minatosato / cymf

Cython implementation of matrix-factorization based algorithms

CyMF

	Ubuntu	macOS
Build status

Cython implementation of matrix-factorization based algorithms.

Implicit Recommender Learning

• Bayesian Personlized Ranking (BPR) [Steffen Rendle et al. 2009]
• Weighted Matrix Factorization (WMF) [Yifan Hu et al. 2008]
• Exposure Matrix Factorization (ExpoMF) [Dawen Liang et al. 2016]
• Relevance Matrix Factorization (RelMF) [Yuta Saito et al. 2019]

Word Embeddings

• GloVe [Jeffrey Pennington et al. 2014]

Requiremts

• GCC >= 7.4.0
• OpenMP
• OpenBLAS
• Python >= 3.7
• Python packages
  • see requirements.txt

Installation

macOS

brew install libomp openblas
echo "export LDFLAGS='-L/usr/local/opt/openblas/lib'" >> ~/.bash_profile
echo "export CPPFLAGS='-I/usr/local/opt/openblas/include'" >> ~/.bash_profile
source ~/.bash_profile

pip install numpy scipy cython
pip install git+https://github.com/satopirka/cymf

Ubuntu

sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt update
sudo apt install -y g++-7
echo "export CXX='g++-7'" >> ~/.bashrc
echo "export CC='gcc-7'" >> ~/.bashrc
source ~/.bashrc
sudo apt install libomp-dev libopenblas-base libopenblas-dev libatlas-base-dev

pip install numpy scipy cython
pip install git+https://github.com/satopirka/cymf

Quickstart

import cymf

dataset = cymf.dataset.MovieLens("ml-100k")

evaluator = cymf.evaluator.AverageOverAllEvaluator(dataset.test, dataset.train, k=5)
model = cymf.BPR(num_components=20, learning_rate=0.01, weight_decay=0.01)
model.fit(dataset.train, num_epochs=30, num_threads=8, verbose=True)
print(evaluator.evaluate(model.W, model.H))

# ITER=30, LOSS: 0.2627: 100%|█████████████████████████████████████████████| 30/30 [00:00<00:00, 98.46it/s]
# {'DCG@5': 0.1815916629140773, 'Recall@5': 0.24528176175220465, 'MAP@5': 0.21311784866390876}