Get Started
Here we describe how to install RecStudio, train and evaluate models built into RecStudio.
Install RecStudio
Clone RecStudio
To use RecStudio, you are required to clone RecStudio source code from our repo.
git clone https://github.com/ustcml/RecStudio.git
Configure the environment
Set up the environment required to run RecStudio.
Run the following code to set up the conda environment.
conda env create -f environment.yml
Run RecStudio
The following is an example of running a built-in model (BPR) of RecStudio.
Run in commond line
We provide the run.py script for users to quickly use the built-in models.
This case trains and evaluates the BPR model on the ml-100k dataset.
python run.py -m=BPR -d=ml-100k
You can modify the parameters to choose different models and datasets. E.g:
python run.py -m=FM -d=ml-1m
Run in .py file
You can use the quickstart module to run a built-in model:
from recstudio import quickstart
quickstart.run(model='LR', dataset='ml-100k')
Different models and datasets can be set by modifying the corresponding parameters:
from recstudio import quickstart
quickstart.run(model='BPR', dataset='ml-1m')