:py:mod:`neural_compressor.strategy.bayesian`
=============================================

.. py:module:: neural_compressor.strategy.bayesian

.. autoapi-nested-parse::

   The Bayesian tuning strategy.



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

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.strategy.bayesian.BayesianTuneStrategy
   neural_compressor.strategy.bayesian.TargetSpace
   neural_compressor.strategy.bayesian.BayesianOptimization



Functions
~~~~~~~~~

.. autoapisummary::

   neural_compressor.strategy.bayesian.acq_max



.. py:class:: BayesianTuneStrategy(model, conf, q_dataloader=None, q_func=None, eval_func=None, eval_dataloader=None, eval_metric=None, resume=None, q_hooks=None)




   The Bayesian tuning strategy.


.. py:function:: acq_max(ac, gp, y_max, bounds, random_seed, n_warmup=10000, n_iter=10)

   Find the maximum of the acquisition function parameters.

   :param ac: The acquisition function object that return its point-wise value.
   :param gp: A gaussian process fitted to the relevant data.
   :param y_max: The current maximum known value of the target function.
   :param bounds: The variables bounds to limit the search of the acq max.
   :param random_seed: instance of np.RandomState random number generator
   :param n_warmup: number of times to randomly sample the acquisition function
   :param n_iter: number of times to run scipy.minimize

   :returns: The arg max of the acquisition function.
   :rtype: x_max


.. py:class:: TargetSpace(pbounds, random_seed=9527)




   Holds the param-space coordinates (X) and target values (Y).

   Allows for constant-time appends while ensuring no duplicates are added.


.. py:class:: BayesianOptimization(pbounds, random_seed=9527, verbose=2)


   The class for bayesian optimization.

   This class takes the parameters bounds in order to find which values for
   the parameters yield the maximum value using bayesian optimization.