class_docstr: .. include:: /global.rst
  
  This solver provides an interface to BayesOpt, as described in [BO2014]_.
  This solver uses BayesOpt in the back-end and exposes its solver with uniform priors.
  
  Please refer to |bayesopt| for details about this algorithm.
  
  .. note::
  
      This solver will always output some text upon running.
      This is caused internally by BayesOpt, which provides no way to disable all output.
  
  .. [BO2014] Martinez-Cantin, Ruben. "BayesOpt: A Bayesian optimization library for nonlinear optimization, experimental design and bandits." The Journal of Machine Learning Research 15.1 (2014): 3735-3739.
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docstr: Verify that we can effectively make a solver from box.
  
  >>> s = BayesOpt.suggest_from_box(30, x=[0, 1], y=[-1, 0], z=[-1, 1])
  >>> solver = BayesOpt(**s) #doctest:+SKIP
docstr_tok: ["Verify","that","we","can","effectively","make","a","solver","from","box",".",">",">",">","s","=","BayesOpt.suggest_from_box","(","30",",","x=","[","0",",","1","]",",","y=","[","-1",",","0","]",",","z=","[","-1",",","1","]",")",">",">",">","solver","=","BayesOpt","(","*","*","s",")","#","doctest",":","+SKIP"]
code: def suggest_from_box(num_evals, **kwargs):
      """
      Verify that we can effectively make a solver from box.
  
      >>> s = BayesOpt.suggest_from_box(30, x=[0, 1], y=[-1, 0], z=[-1, 1])
      >>> solver = BayesOpt(**s) #doctest:+SKIP
  
      """
      d = dict(kwargs)
      d["num_evals"] = num_evals
      return d
  
file_path: optunity/wrappers/R/inst/optunity/solvers/BayesOpt.py
imports: from .solver_registry import register_solver
  from .util import Solver, _copydoc
  import functools
  import random
label_desc: Use image node_id 2 for calling the BayesOpt obj's underlying member method code with example usage: obj.suggest_from_box(num_evals) and returns: d