.. fusion-opt documentation master file, created by
   sphinx-quickstart on Mon Jun  3 11:44:00 2024.
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Welcome to fusion-opt's documentation!
======================================

Fusion-opt is a framework for applying Meta-learning based bayesian optimization equipped with humnan feedback capability during optimization, to guide the optimization specially in the initial stages
and also to have a warm start.


Main contributions of fusion-opt:

- Using state of the art Neural Acquistion process(NAP) as a surrogate for  black box functions
- Using preference model trained on experts' feedback to enable scoring preference of the points to query next 
- Suggesting multiple candidate points to human(expert), from which one to be selected as next point to be queried 
- A newly designed Acquistion Function combines the preference model and the NAP to suggest one candidate point
- The other candidate points are chosen by desired well-known statistical or Monte-carlo based Acquistion functions such as EI, MES, ...
- Modular design, i.e users can utilize pre-defined modules for preference models, or build their own
- Explainability, the framework explains each of the candidate points so that human in the loop can make better decsion for selecting the next point

.. image:: images/components.png
   :scale: 100 %
   :alt: Components
   :align: center


.. image:: images/explainability.png
   :scale: 100 %
   :alt: Explainable BO
   :align: center




.. toctree::
   :hidden:

   installation
   overview
   train_evaluate/index
   usage/index 
   code/index
   references