docstr: Defaults copied from BOHB constructor, see details in BOHB.__init__
  
  BOHB performs robust and efficient hyperparameter optimization
  at scale by combining the speed of Hyperband searches with the
  guidance and guarantees of convergence of Bayesian
  Optimization. Instead of sampling new configurations at random,
  BOHB uses kernel density estimators to select promising candidates.
  
  .. note::
  
      For reference:
      @InProceedings{falkner-icml-18,
        title =        {{BOHB}: Robust and Efficient Hyperparameter Optimization at Scale},
        author =       {Falkner, Stefan and Klein, Aaron and Hutter, Frank},
        booktitle =    {Proceedings of the 35th International Conference on Machine Learning},
        pages =        {1436--1445},
        year =         {2018},
      }
  
  
  :param eta: float (3)
      In each iteration, a complete run of sequential halving is executed. In it,
      after evaluating each configuration on the same subset size, only a fraction of
      1/eta of them 'advances' to the next round.
      Must be greater or equal to 2.
  :param min_budget: float (0.01)
      The smallest budget to consider. Needs to be positive!
  :param max_budget: float (1)
      The largest budget to consider. Needs to be larger than min_budget!
      The budgets will be geometrically distributed
      :math:`a^2 + b^2 = c^2 /sim /eta^k` for :math:`k/in [0, 1, ... , num/_subsets - 1]`.
  :param min_points_in_model: int (None)
      number of observations to start building a KDE. Default 'None' means
      dim+1, the bare minimum.
  :param top_n_percent: int (15)
      percentage ( between 1 and 99, default 15) of the observations that are considered good.
  :param num_samples: int (64)
      number of samples to optimize EI (default 64)
  :param random_fraction: float (1/3.)
      fraction of purely random configurations that are sampled from the
      prior without the model.
  :param bandwidth_factor: float (3.)
      to encourage diversity, the points proposed to optimize EI, are sampled
      from a 'widened' KDE where the bandwidth is multiplied by this factor (default: 3)
  :param min_bandwidth: float (1e-3)
      to keep diversity, even when all (good) samples have the same value for one of the parameters,
      a minimum bandwidth (Default: 1e-3) is used instead of zero.
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code: def set_optimization_args(
      self,
      eta=3,  # type: float
      min_budget=None,  # type: Optional[float]
      max_budget=None,  # type: Optional[float]
      min_points_in_model=None,  # type: Optional[int]
      top_n_percent=15,  # type: Optional[int]
      num_samples=None,  # type: Optional[int]
      random_fraction=1 / 3.0,  # type: Optional[float]
      bandwidth_factor=3,  # type: Optional[float]
      min_bandwidth=1e-3,  # type: Optional[float]
  ):
      # type: (...) -> ()
      """
      Defaults copied from BOHB constructor, see details in BOHB.__init__
  
      BOHB performs robust and efficient hyperparameter optimization
      at scale by combining the speed of Hyperband searches with the
      guidance and guarantees of convergence of Bayesian
      Optimization. Instead of sampling new configurations at random,
      BOHB uses kernel density estimators to select promising candidates.
  
      .. note::
  
          For reference:
          @InProceedings{falkner-icml-18,
            title =        {{BOHB}: Robust and Efficient Hyperparameter Optimization at Scale},
            author =       {Falkner, Stefan and Klein, Aaron and Hutter, Frank},
            booktitle =    {Proceedings of the 35th International Conference on Machine Learning},
            pages =        {1436--1445},
            year =         {2018},
          }
  
  
      :param eta: float (3)
          In each iteration, a complete run of sequential halving is executed. In it,
          after evaluating each configuration on the same subset size, only a fraction of
          1/eta of them 'advances' to the next round.
          Must be greater or equal to 2.
      :param min_budget: float (0.01)
          The smallest budget to consider. Needs to be positive!
      :param max_budget: float (1)
          The largest budget to consider. Needs to be larger than min_budget!
          The budgets will be geometrically distributed
          :math:`a^2 + b^2 = c^2 /sim /eta^k` for :math:`k/in [0, 1, ... , num/_subsets - 1]`.
      :param min_points_in_model: int (None)
          number of observations to start building a KDE. Default 'None' means
          dim+1, the bare minimum.
      :param top_n_percent: int (15)
          percentage ( between 1 and 99, default 15) of the observations that are considered good.
      :param num_samples: int (64)
          number of samples to optimize EI (default 64)
      :param random_fraction: float (1/3.)
          fraction of purely random configurations that are sampled from the
          prior without the model.
      :param bandwidth_factor: float (3.)
          to encourage diversity, the points proposed to optimize EI, are sampled
          from a 'widened' KDE where the bandwidth is multiplied by this factor (default: 3)
      :param min_bandwidth: float (1e-3)
          to keep diversity, even when all (good) samples have the same value for one of the parameters,
          a minimum bandwidth (Default: 1e-3) is used instead of zero.
  
      """
      if min_budget:
          self._bohb_kwargs["min_budget"] = min_budget
      if max_budget:
          self._bohb_kwargs["max_budget"] = max_budget
      if num_samples:
          self._bohb_kwargs["num_samples"] = num_samples
      self._bohb_kwargs["eta"] = eta
      self._bohb_kwargs["min_points_in_model"] = min_points_in_model
      self._bohb_kwargs["top_n_percent"] = top_n_percent
      self._bohb_kwargs["random_fraction"] = random_fraction
      self._bohb_kwargs["bandwidth_factor"] = bandwidth_factor
      self._bohb_kwargs["min_bandwidth"] = min_bandwidth
  
file_path: clearml/clearml/automation/hpbandster/bandster.py
imports: from time import sleep, time
  from typing import Any, Optional, Sequence
  from ..optimization import Objective, SearchStrategy
  from ..parameters import DiscreteParameterRange, UniformParameterRange, RandomSeed, UniformIntegerParameterRange, Parameter
  from ...task import Task
label_desc: Use image node_id 2 for calling the OptimizerBOHB obj's underlying member method code with example usage: obj.set_optimization_args(eta, min_budget, max_budget, min_points_in_model, top_n_percent, num_samples, random_fraction, bandwidth_factor, min_bandwidth) without return types