:py:mod:`cotengra.hyperoptimizers.hyper_nevergrad`
==================================================

.. py:module:: cotengra.hyperoptimizers.hyper_nevergrad

.. autoapi-nested-parse::

   Hyper optimization using nevergrad.



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


Functions
~~~~~~~~~

.. autoapisummary::

   cotengra.hyperoptimizers.hyper_nevergrad.convert_param_to_nevergrad
   cotengra.hyperoptimizers.hyper_nevergrad.get_methods_space
   cotengra.hyperoptimizers.hyper_nevergrad.convert_to_nevergrad_space
   cotengra.hyperoptimizers.hyper_nevergrad.nevergrad_init_optimizers
   cotengra.hyperoptimizers.hyper_nevergrad.nevergrad_get_setting
   cotengra.hyperoptimizers.hyper_nevergrad.nevergrad_report_result



.. py:function:: convert_param_to_nevergrad(param)


.. py:function:: get_methods_space(methods)


.. py:function:: convert_to_nevergrad_space(method, space)


.. py:function:: nevergrad_init_optimizers(self, methods, space, sampler='NaiveTBPSA', method_sampler=None, budget='auto', num_workers=1, method_budget='auto', method_num_workers=1, sampler_opts=None, method_sampler_opts=None)

   Initialize the ``nevergrad`` optimizer.

   :param space: The search space.
   :type space: dict[str, dict[str, dict]]
   :param sampler: The optimizer to use to search each method's search space, see
                   https://facebookresearch.github.io/nevergrad/optimizers_ref.html.
   :type sampler: str, optional
   :param method_sampler: The meta-optimizer to use to select overall methods.
   :param str: The meta-optimizer to use to select overall methods.
   :param optional: The meta-optimizer to use to select overall methods.
   :param budget: Supplied to optimizer.
   :type budget: int, optional
   :param num_workers: Supplied to optimizer.
   :type num_workers: int, optional
   :param method_budget: Supplied to meta-optimizer.
   :type method_budget: int, optional
   :param method_num_workers: Supplied to meta-optimizer.
   :type method_num_workers: int, optional


.. py:function:: nevergrad_get_setting(self)

   Get a setting to trial from one of the nevergrad optimizers.


.. py:function:: nevergrad_report_result(self, setting, trial, score)

   Report the result of a trial to the ``nevergrad`` optimizers.