A Pytorch Backend Library for Choice Modelling with Bayesian Matrix Factorization
Project description
Bayesian Embedding (BEMB)
Authors: Tianyu Du and Ayush Kanodia; PI: Susan Athey; Contact: tianyudu@stanford.edu
BEMB is a flexible, fast Bayesian embedding model for modelling choice problems. The bemb package is built upon the torch_choice library.
The full documentation website for BEMB is https://gsbdbi.github.io/bemb/.
Installation
- Install
torch-choicefollowing steps here. - Run the following script to install it.
# Clone the repository to your local machine or server for tutorials.
git clone "git@github.com:gsbDBI/bemb.git"
# Install required dependencies.
pip3 install -r requirements.txt
# Install bemb from the Pip.
pip3 install bemb
# Check installation.
python3 -c 'import torch_choice; print(bemb.__version__)'
Example Usage of BEMB
Here is a simulation exercise of using bemb.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
bemb-0.1.4.tar.gz
(39.5 kB
view hashes)
Built Distribution
bemb-0.1.4-py3-none-any.whl
(43.5 kB
view hashes)
