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Installation

The packages requires a python version >=3.5, as well as some libraries listed in requirements file. For some additional functionalities, more libraries are needed for these extra functions and options to become available. Here is a quick installation guide for the package, starting off with the minimal install up to the full installation.

Note: A (mini/ana)conda framework would help installing all those packages and therefore could be recommended for non-expert users.

Miniconda installation [optional]

As miniconda is lighter and has most of the interesting functionalities of anaconda, we would recommend using Miniconda.

  1. Go to https://conda.io/miniconda.html and download the latest version of the software in 64bits. The python version here (2.7 or 3.6) does not make any importance at this stage.
  2. Go to your downloaded folder and execute the installation script. For linux users, in a terminal:
$ bash Miniconda-X.sh
  1. Start off by creating your first environment named 'py35': open a (new, or refresh the .bashrc) terminal and type:
$ conda create --name py35 python=3.5
  1. Activate your new environment by using the command:
$ source activate py35

Note: Keep your environment activated in order to install all your libraries in your environment.

Python 3.5 or higher

If you installed a Miniconda framework at the last step, you already installed python3.5 in an environment, and therefore you can skip this step.

To check if python 3.5 is installed on your device, execute the command:

$ python --version

If not, you can install it with the command (Debian distribution):

$ sudo apt-get install python3 python3-pip python3-wheel

Install PyTorch

As some of the key algorithms in the cdt package use the PyTorch package, it is required to install it. Check out their website to install the PyTorch version suited to your hardware configuration: http://pytorch.org

Install the CausalDiscoveryToolboxPackage

$ git clone https://github.com/FenTechSolutions/CausalDiscoveryToolbox.git  # Download the package 
$ cd CausalDiscoveryToolbox
$ pip install -r requirements.txt  # Install the requirements
$ python setup.py install develop --user

The package is then up and running ! You can run most of the algorithms in the CausalDiscoveryToolbox, you might get warnings: some additional features are not available

From now on, you can import the library using :

import cdt

Check out the package structure and more info on the package itself here.

Additional : R and R libraries

In order to have access to additional algorithms from various R packages such as bnlearn, kpcalg, pcalg, ... while using the cdt framework, it is required to install R.

Using bioconductor (https://www.bioconductor.org/) might be an easier alternative to install all packages. Check out the r-requirements file to see all the packages used by the toolbox.