code: def _skopt_opdefs():
      opdefs = yaml.safe_load(
          """
      random:
        description:
          Batch processor supporting random flag value generation.
        exec: ${guild_python_exe} -um guild.plugins.random_main
        delete-on-success: yes
        can-stage-trials: yes
  
      gp:
        description:
          Bayesian optimizer using Gaussian processes.
  
          Refer to https://scikit-optimize.github.io/#skopt.gp_minimize
          for details on this algorithm and its flags.
        exec: ${guild_python_exe} -um guild.plugins.skopt_gp_main
        flags:
          random-starts:
            description:
              Number of trials using random values before optimizing
            default: 3
            type: int
          acq-func:
            description:
              Function to minimize over the gaussian prior
            default: gp_hedge
            choices:
              - value: LCB
                description: Lower confidence bound
              - value: EI
                description: Negative expected improvement
              - value: PI
                description: Negative probability of improvement
              - value: gp_hedge
  
                description:
                  Probabilistically use LCB, EI, or PI at every
                  iteration
              - value: EIps
                description: Negative expected improvement per second
              - value: PIps
                description:
                  Negative probability of improvement per second
          kappa:
            description:
              Degree to which variance in the predicted values is taken
              into account
            default: 1.96
            type: float
          xi:
            description:
              Improvement to seek over the previous best values
            default: 0.05
            type: float
          noise:
            description:
              Level of noise associated with the objective
  
              Use 'gaussian' if the objective returns noisy
              observations, otherwise specify the expected variance of
              the noise.
            default: gaussian
          prev-trials:
            description:
              Method used to select previous trials for suggestions
            default: sourcecode
            choices:
              - value: batch
                description: Use trials generated by the batch run
              - value: sourcecode
                description: Use trials with the same source code
              - value: operation
                description: Use trials with the same operation name
  
      forest:
        description:
          Sequential optimization using decision trees.
  
          Refer to
          https://scikit-optimize.github.io/#skopt.forest_minimize for
          details on this algorithm and its flags.
  
        exec: ${guild_python_exe} -um guild.plugins.skopt_forest_main
        flags:
          random-starts:
            description:
              Number of trials using random values before optimizing
            default: 3
            type: int
          kappa:
            description:
              Degree to which variance in the predicted values is
              taken into account
            default: 1.96
            type: float
          xi:
            description:
              Improvement to seek over the previous best values
            default: 0.05
            type: float
          prev-trials:
            description:
              Method used to select previous trials for suggestions
            default: sourcecode
            choices:
              - value: batch
                description: Use trials generated by the batch run
              - value: sourcecode
                description: Use trials with the same source code
              - value: operation
                description: Use trials with the same operation name
      gbrt:
        description:
          Sequential optimization using gradient boosted regression
          trees.
  
          Refer to
          https://scikit-optimize.github.io/#skopt.gbrt_minimize for
          details on this algorithm and its flags.
  
        exec: ${guild_python_exe} -um guild.plugins.skopt_gbrt_main
        flags:
          random-starts:
            description:
              Number of trials using random values before optimizing
            default: 3
            type: int
          kappa:
            description:
              Degree to which variance in the predicted values is taken
              into account
            default: 1.96
            type: float
          xi:
            description:
              Improvement to seek over the previous best values
            default: 0.05
            type: float
          prev-trials:
            description:
              Method used to select previous trials for suggestions
            default: sourcecode
            choices:
              - value: batch
                description: Use trials generated by the batch run
              - value: sourcecode
                description: Use trials with the same source code
              - value: operation
                description: Use trials with the same operation name
      """
      )
      _apply_opdef_defaults(opdefs)
      return opdefs
  
file_path: guildai/guild/plugins/skopt.py
imports: import yaml
  import guild
  from guild import flag_util
  from guild import model
  from guild import model_proxy
  from guild import plugin
label_desc: Use image node_id 1 for calling a global function with example usage: _skopt_opdefs() and returns: opdefs