branin
======

.. py:module:: branin

.. autoapi-nested-parse::

   Branin objective function, relying on the botorch implementation.

   This code is based on the implementation by Nikita Saxena (nikita-0209) in
   https://github.com/alexhernandezgarcia/activelearning

   The implementation assumes by default that the inputs will be on [-1, 1] x [-1, 1] and
   will be mapped to the standard domain of the Branin function (see X1_DOMAIN and
   X2_DOMAIN). Setting do_domain_map to False will prevent the mapping.

   Branin function is typically used as a minimization problem, with the minima around
   zero but positive. By default, proxy values remain in this range and the reward is
   mapped to (~0, UPPER_BOUND_IN_DOMAIN). The user should carefully select the reward
   function in order to stick to the conventional use of the Branin function.



Attributes
----------

.. autoapisummary::

   branin.X1_DOMAIN
   branin.X1_LENGTH
   branin.X2_DOMAIN
   branin.X2_LENGTH
   branin.UPPER_BOUND_IN_DOMAIN
   branin.OPTIMUM
   branin.MODES


Classes
-------

.. autoapisummary::

   branin.Branin


Module Contents
---------------

.. py:data:: X1_DOMAIN

.. py:data:: X1_LENGTH
   :value: 15


.. py:data:: X2_DOMAIN
   :value: [0, 15]


.. py:data:: X2_LENGTH
   :value: 15


.. py:data:: UPPER_BOUND_IN_DOMAIN
   :value: 309


.. py:data:: OPTIMUM
   :value: 0.397887


.. py:data:: MODES
   :value: [[12.4, 81.833], [54.266, 15.16], [94.98, 16.5]]


.. py:class:: Branin(fidelity = 1.0, do_domain_map = True, negate = False, reward_function = lambda x: -1.0 * (x - UPPER_BOUND_IN_DOMAIN), **kwargs)

   Bases: :py:obj:`gflownet.proxy.base.Proxy`


   :param fidelity: Fidelity of the Branin oracle. 1.0 corresponds to the original Branin.
                    Smaller values (up to 0.0) reduce the fidelity of the oracle.
   :type fidelity: float
   :param do_domain_map: If True, the states are assumed to be in [-1, 1] x [-1, 1] and are re-mapped
                         to the standard domain before calling the botorch method. If False, the
                         botorch method is called directly on the states values.
   :type do_domain_map: bool
   :param negate: If True, proxy values are multiplied by -1.
   :type negate: bool
   :param reward_function: The transformation applied to the proxy outputs to obtain a GFlowNet
                           reward. By default, proxy values are shifted by UPPER_BOUND_IN_DOMAIN and
                           multiplied by minus one, in order to make them positive and the higher the
                           better.
   :type reward_function: str or Callable
   :param See:
   :type See: https://botorch.org/api/test_functions.html


   .. py:attribute:: fidelity
      :value: 1.0



   .. py:attribute:: do_domain_map
      :value: True



   .. py:attribute:: function_mf_botorch


   .. py:attribute:: domain_left


   .. py:attribute:: domain_length


   .. py:method:: __call__(states)

      Implement  this function to call the get_reward method of the appropriate Proxy
      Class (EI, UCB, Proxy, Oracle etc).

      :param states:
      :type states: ndarray



   .. py:method:: map_to_standard_domain(states)

      Maps a batch of input states onto the domain typically used to evaluate the
      Branin function. See X1_DOMAIN and X2_DOMAIN. It assumes that the inputs are on
      [-1, 1] x [-1, 1].