cotengra.hyperoptimizers.hyper_cmaes
====================================

.. py:module:: cotengra.hyperoptimizers.hyper_cmaes

.. autoapi-nested-parse::

   Hyper parameter optimization using cmaes, as implemented by

   https://github.com/CyberAgentAILab/cmaes.



Classes
-------

.. autoapisummary::

   cotengra.hyperoptimizers.hyper_cmaes.LCBOptimizer
   cotengra.hyperoptimizers.hyper_cmaes.Param
   cotengra.hyperoptimizers.hyper_cmaes.ParamFloat
   cotengra.hyperoptimizers.hyper_cmaes.ParamFloatExp
   cotengra.hyperoptimizers.hyper_cmaes.ParamInt
   cotengra.hyperoptimizers.hyper_cmaes.ParamString
   cotengra.hyperoptimizers.hyper_cmaes.ParamBool
   cotengra.hyperoptimizers.hyper_cmaes.HyperCMAESSampler


Functions
---------

.. autoapisummary::

   cotengra.hyperoptimizers.hyper_cmaes.cmaes_init_optimizers
   cotengra.hyperoptimizers.hyper_cmaes.cmaes_get_setting
   cotengra.hyperoptimizers.hyper_cmaes.cmaes_report_result


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

.. py:class:: LCBOptimizer(options, exploration=1.0, temperature=1.0, seed=None)

   Lower Confidence Bound Optimizer.

   This optimizer selects the option with the lowest lower confidence bound.


   .. py:attribute:: options


   .. py:attribute:: index


   .. py:attribute:: nopt


   .. py:attribute:: counts


   .. py:attribute:: values


   .. py:attribute:: total
      :value: 0



   .. py:attribute:: exploration
      :value: 1.0



   .. py:attribute:: temperature
      :value: 1.0



   .. py:attribute:: rng


   .. py:method:: ask()

      Suggest an option based on the lower confidence bound.



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


.. py:class:: Param(name)

   A basic parameter class for mapping various types of parameters to
   and from uniform optimization space of [-1, 1].


   .. py:attribute:: name


   .. py:attribute:: size
      :value: 1



   .. py:method:: get_raw_bounds()
      :abstractmethod:



   .. py:method:: convert_raw(vi)
      :abstractmethod:



.. py:class:: ParamFloat(min, max, **kwargs)

   Bases: :py:obj:`Param`


   A basic parameter class for mapping various types of parameters to
   and from uniform optimization space of [-1, 1].


   .. py:attribute:: min


   .. py:attribute:: max


   .. py:method:: convert_raw(x)


.. py:class:: ParamFloatExp(min, max, power=0.5, **kwargs)

   Bases: :py:obj:`ParamFloat`


   An exponentially distributed (i.e. uniform in logspace) parameter.


   .. py:attribute:: power
      :value: 0.5



   .. py:method:: convert_raw(x)


.. py:class:: ParamInt(min, max, **kwargs)

   Bases: :py:obj:`Param`


   A basic parameter class for mapping various types of parameters to
   and from uniform optimization space of [-1, 1].


   .. py:attribute:: min


   .. py:attribute:: max


   .. py:method:: convert_raw(x)


.. py:class:: ParamString(options, name)

   Bases: :py:obj:`Param`


   A basic parameter class for mapping various types of parameters to
   and from uniform optimization space of [-1, 1].


   .. py:attribute:: options


   .. py:attribute:: size


   .. py:attribute:: name


   .. py:method:: convert_raw(x)


.. py:class:: ParamBool(name)

   Bases: :py:obj:`Param`


   A basic parameter class for mapping various types of parameters to
   and from uniform optimization space of [-1, 1].


   .. py:attribute:: size
      :value: 2



   .. py:attribute:: name


   .. py:method:: convert_raw(x)


.. py:class:: HyperCMAESSampler(space, sigma=1.0, lr_adapt=True, separable=False, exponential_param_power=None, **kwargs)

   .. py:attribute:: params
      :value: []



   .. py:attribute:: opt


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



   .. py:attribute:: _trial_store


   .. py:attribute:: _batch
      :value: []



   .. py:method:: ask()


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


.. py:function:: cmaes_init_optimizers(self, methods, space, sigma=1.0, lr_adapt=True, method_exploration=1.0, method_temperature=1.0, exponential_param_power=None, **cmaes_opts)

.. py:function:: cmaes_get_setting(self)

.. py:function:: cmaes_report_result(self, settings, trial, score)