class_docstr: Hyperopt: Distributed Asynchronous Hyper-parameter Optimization.
  This class is a wrapper over the `hyperopt <https://github.com/hyperopt/hyperopt>`_ package.
  
  Parameters
  ----------
  parametrization: int or Parameter
      Parametrization object
  budget: int
      Number of iterations
  num_workers: int
      Number of workers
  prior_weight: float (default 1.0)
      Smoothing factor to avoid having zero probabilities
  n_startup_jobs: int (default 20)
      Number of random uniform suggestions at initialization
  n_EI_candidates: int (default 24)
      Number of generated candidates during EI maximization
  gamma: float (default 0.25)
      Threshold to split between l(x) and g(x), see eq. 2 in
  
  verbose: bool (default False)
      Hyperopt algorithm verbosity
  
  Note
  ----
  HyperOpt is described in Bergstra, James S., et al.
  "Algorithms for hyper-parameter optimization."
  Advances in neural information processing systems. 2011
class_docstr_tok: ["Hyperopt",":","Distributed","Asynchronous","Hyper-parameter","Optimization",".","This","class","is","a","wrapper","over","the","`","hyperopt","<","https",":","\/\/github.com\/hyperopt\/hyperopt",">","`","_","package",".","Parameters","--","--","--","--","--","parametrization",":","int","or","Parameter","Parametrization","object","budget",":","int","Number","of","iterations","num_workers",":","int","Number","of","workers","prior_weight",":","float","(","default","1.0",")","Smoothing","factor","to","avoid","having","zero","probabilities","n_startup_jobs",":","int","(","default","20",")","Number","of","random","uniform","suggestions","at","initialization","n_EI_candidates",":","int","(","default","24",")","Number","of","generated","candidates","during","EI","maximization","gamma",":","float","(","default","0.25",")","Threshold","to","split","between","l","(","x",")","and","g","(","x",")",",","see","eq",".","2","in","verbose",":","bool","(","default","False",")","Hyperopt","algorithm","verbosity","Note","--","--","HyperOpt","is","described","in","Bergstra",",","James","S.",",","et","al",".","``","Algorithms","for","hyper-parameter","optimization",".","''","Advances","in","neural","information","processing","systems",".","2011"]
code: def __init__(
      self,
      *,
      prior_weight: float = 1.0,
      n_startup_jobs: int = 20,
      n_EI_candidates: int = 24,
      gamma: float = 0.25,
      verbose: bool = False,
  ) -> None:
      super().__init__(_HyperOpt, locals())
  
imports: import numpy
  from .None import base
  from .base import IntOrParameter
  import nevergrad.common.typing
  from nevergrad.parametrization import transforms
  from nevergrad.parametrization import parameter
  import hyperopt
  from hyperopt import hp, Trials, Domain, tpe
label_desc: Use image node_id 1 to create a new ParametrizedHyperOpt object from inherited base classes: base with example: obj = ParametrizedHyperOpt()