cotengra.experimental.hyper_pymoo
=================================

.. py:module:: cotengra.experimental.hyper_pymoo

.. autoapi-nested-parse::

   Hyper optimization using pymoo single-objective algorithms.

   This backend currently supports serial optimization only. Pymoo ask/tell
   algorithms operate on generations/batches rather than individual trials, so
   the integration buffers one full batch at a time and feeds it back when all
   batch members have been evaluated.



Classes
-------

.. autoapisummary::

   cotengra.experimental.hyper_pymoo.HyperPymooSampler
   cotengra.experimental.hyper_pymoo.PymooOptLib


Functions
---------

.. autoapisummary::

   cotengra.experimental.hyper_pymoo._get_pymoo_algorithm


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

.. py:function:: _get_pymoo_algorithm(name)

.. py:class:: HyperPymooSampler(space, sampler='de', sampler_opts=None, exponential_param_power=None, seed=None)

   Per-method ask/tell wrapper around a pymoo algorithm.


   .. py:attribute:: _np


   .. py:attribute:: _Evaluator


   .. py:attribute:: _StaticProblem


   .. py:attribute:: params


   .. py:attribute:: _ndim


   .. py:attribute:: _problem


   .. py:attribute:: algorithm


   .. py:attribute:: _trial_counter
      :value: 0



   .. py:attribute:: _active_batch
      :value: None



   .. py:method:: ask()


   .. py:method:: tell(trial_number, score)


.. py:class:: PymooOptLib

   Bases: :py:obj:`cotengra.experimental.hyper.HyperOptLib`


   Hyper-optimization using pymoo algorithms with LCB method choice.


   .. py:method:: setup(methods, space, optimizer=None, sampler='de', sampler_opts=None, method_exploration=1.0, method_temperature=1.0, exponential_param_power=None, seed=None, **kwargs)


   .. py:method:: get_setting()


   .. py:method:: report_result(setting, trial, score)