chpi is clustering part of implementation for paper: "To tune or not to tune? An Approach for Recommending Important Hyperparameters for Classification and Clustering Algorithms"
All source code used to generate the results and figures in the paper are in
the chpi folder.
You can download a copy of all the files in this repository by cloning this repo.
git clone https://github.com/DataSystemsGroupUT/chpi
You'll need a working Python environment to run the code.
The required dependencies are specified in the file environment.yml.
We use conda virtual environments to manage the project dependencies in
isolation.
Thus, you can install our dependencies without causing conflicts with your
setup (even with different Python versions).
Run the following command in the repository folder (where environment.yml
is located) to create a separate environment and install all required
dependencies in it:
conda env create -f environment.yml
Before running any code you must activate the conda environment:
source activate ENVIRONMENT_NAME
or, if you're on Windows:
activate ENVIRONMENT_NAME
This will enable the environment for your current terminal session. Any subsequent commands will use software that is installed in the environment.
Another way of exploring the code results is to execute the Jupyter notebooks individually. To do this, you must first start the notebook server by going into the repository top level and running:
jupyter notebook
All source code is made available under a MIT license. You can freely
use and modify the code, without warranty, so long as you provide attribution
to the authors. See LICENSE.md for the full license text.
The manuscript text is not open source. The authors reserve the rights to the article content, which is currently submitted for publication.