mlos_core.optimizers.random_optimizer
=====================================

.. py:module:: mlos_core.optimizers.random_optimizer

.. autoapi-nested-parse::

   RandomOptimizer class.



Classes
-------

.. autoapisummary::

   mlos_core.optimizers.random_optimizer.RandomOptimizer


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

.. py:class:: RandomOptimizer(*, parameter_space: ConfigSpace.ConfigurationSpace, optimization_targets: list[str], objective_weights: list[float] | None = None, space_adapter: mlos_core.spaces.adapters.adapter.BaseSpaceAdapter | None = None)

   Bases: :py:obj:`mlos_core.optimizers.optimizer.BaseOptimizer`


   Optimizer class that produces random suggestions.

   Useful for baseline comparison against Bayesian optimizers.

   Create a new instance of the base optimizer.

   :param parameter_space: The parameter space to optimize.
   :type parameter_space: ConfigSpace.ConfigurationSpace
   :param optimization_targets: The names of the optimization targets to minimize.
                                To maximize a target, use the negative of the target when registering scores.
   :type optimization_targets: list[str]
   :param objective_weights: Optional list of weights of optimization targets.
   :type objective_weights: Optional[list[float]]
   :param space_adapter: The space adapter class to employ for parameter space transformations.
   :type space_adapter: BaseSpaceAdapter


   .. py:method:: register_pending(pending: mlos_core.data_classes.Suggestion) -> None
      :abstractmethod:


      Registers the given suggestion as "pending". That is it say, it has been
      suggested by the optimizer, and an experiment trial has been started. This can
      be useful for executing multiple trials in parallel, retry logic, etc.

      :param pending: The pending suggestion to register.
      :type pending: Suggestion