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Getting Started
================

**Graph-Based Bayesian Illumination** (GB-BI) is an open-source software library that aims to make state-of-the-art, quality-diversity optimisation techniques infused with Bayesian optimisation easily accessible to scientific experts in medicinal chemistry and cheminformatics. The main novelty of Bayesian illumination compared to a previous quality diversity method for small molecule optimisation is the use of surrogate fitness and acquisition function calculations to inform the selection of a single molecule to be compared in direct evolutionary competition with the current occupant of the niche. 

After installing the software and running the tests, a basic usage example of Bayesian Illumination (i.e. the rediscovery of Troglitazone) can be called upon in the following manner:

.. code-block:: bash

    python illuminate.py

This command will call the config file in `configs` and makes use of Hydra for command line overwrites. Hydra is an open-source Python framework, developed and maintained by Meta Research, that simplifies the development of research and other complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line. For examples of how to use the command line for GB-BI or how to change the config files, please consult the tutorials suppied with this documentation.


Installing
----------------------

Download the source code from Github to your local machine and create the environment from the `bayesian_illumination.yml` file:

.. code-block:: bash

    conda env create -f bayesian-illumination.yml

Activate the new environment:

.. code-block:: bash

    conda activate bayesian-illumination

Verify that the new environment was installed correctly:

.. code-block:: bash

    conda env list

Running the Tests
----------------------

To run the unit tests:

.. code-block:: bash

    pytest ./tests