This repository contains supplementary material for the reproducibiliy of computational studies performed in the report Learning high dimensional gaussian graphical models written by Adja Magatte Fall under the supervision of:
- Pierre Fernique,
- Jean Peyhardi.
These studies are formatted as pre-executed Jupyter notebooks. Refers to the index.ipynb notebook which presents and references each study.
Using Docker images and a Binder server, we are able to provide ways to reproduce the article studies without installing the StatisKit software suite.
To reproduce the studies online, use this server.
To reproduce the studies with Docker use these images. After installing Docker, you can type the following command in a shell:
docker run -i -t -p 8888:8888 statiskit/ffp17:latest
Then, follow the given instructions.
You can also install required packages on your computer to reproduce these studies. In order to ease the installation of these packages on multiple operating systems, the Conda package and environment management system is used. For more information refers to the StatisKit software suite documentation concerning prerequisites to the installation step. Then, to install the required packages, proceed as as follows:
Clone this repository,
git clone https://github.com/StatisKit/FFP17
Enter the cloned repository,
cd FPD17
Install the given Conda environment,
conda env create -f environment.yml
Activate the Conda environment as precised in your terminal.
Enter the
share
repository,cd share
Enter the
jupyter
repository,cd jupyter
Launch the Jupyter the index.ipynb notebook,
jupyter notebook index.ipynb
Execute the index.ipynb notebook to execute all examples or navigate among referenced notebooks to execute them separatly.