docstr: It builds the configuration space with the needed hyperparameters.
  It is easily possible to implement different types of hyperparameters.
  Beside float-hyperparameters on a log scale, it is also able to handle categorical input parameter.
  :return: ConfigurationsSpace-Object
docstr_tok: ["It","builds","the","configuration","space","with","the","needed","hyperparameters",".","It","is","easily","possible","to","implement","different","types","of","hyperparameters",".","Beside","float-hyperparameters","on","a","log","scale",",","it","is","also","able","to","handle","categorical","input","parameter",".",":","return",":","ConfigurationsSpace-Object"]
code: def get_configspace():
      """
      It builds the configuration space with the needed hyperparameters.
      It is easily possible to implement different types of hyperparameters.
      Beside float-hyperparameters on a log scale, it is also able to handle categorical input parameter.
      :return: ConfigurationsSpace-Object
      """
      cs = CS.ConfigurationSpace()
  
      lr = CSH.UniformFloatHyperparameter(
          "lr", lower=1e-6, upper=1e-1, default_value="1e-2", log=True
      )
  
      # For demonstration purposes, we add different optimizers as categorical hyperparameters.
      # To show how to use conditional hyperparameters with ConfigSpace, we'll add the optimizers 'Adam' and 'SGD'.
      # SGD has a different parameter 'momentum'.
      optimizer = CSH.CategoricalHyperparameter(
          "optimizer", ["Adam", "SGD"]
      )
  
      sgd_momentum = CSH.UniformFloatHyperparameter(
          "sgd_momentum",
          lower=0.0,
          upper=0.99,
          default_value=0.9,
          log=False,
      )
  
      cs.add_hyperparameters([lr, optimizer, sgd_momentum])
  
      # The hyperparameter sgd_momentum will be used,if the configuration
      # contains 'SGD' as optimizer.
      cond = CS.EqualsCondition(sgd_momentum, optimizer, "SGD")
      cs.add_condition(cond)
  
      num_conv_layers = CSH.UniformIntegerHyperparameter(
          "num_conv_layers", lower=1, upper=3, default_value=2
      )
  
      num_filters_1 = CSH.UniformIntegerHyperparameter(
          "num_filters_1", lower=4, upper=64, default_value=16, log=True
      )
      num_filters_2 = CSH.UniformIntegerHyperparameter(
          "num_filters_2", lower=4, upper=64, default_value=16, log=True
      )
      num_filters_3 = CSH.UniformIntegerHyperparameter(
          "num_filters_3", lower=4, upper=64, default_value=16, log=True
      )
  
      cs.add_hyperparameters(
          [num_conv_layers, num_filters_1, num_filters_2, num_filters_3]
      )
  
      # You can also use inequality conditions:
      cond = CS.GreaterThanCondition(num_filters_2, num_conv_layers, 1)
      cs.add_condition(cond)
  
      cond = CS.GreaterThanCondition(num_filters_3, num_conv_layers, 2)
      cs.add_condition(cond)
  
      dropout_rate = CSH.UniformFloatHyperparameter(
          "dropout_rate",
          lower=0.0,
          upper=0.9,
          default_value=0.5,
          log=False,
      )
      num_fc_units = CSH.UniformIntegerHyperparameter(
          "num_fc_units", lower=8, upper=256, default_value=32, log=True
      )
  
      cs.add_hyperparameters([dropout_rate, num_fc_units])
  
      return cs
  
file_path: HpBandSter/docs/build/html/_downloads/example_5_pytorch_worker.py
imports: import ConfigSpace
  import ConfigSpace.hyperparameters
  from hpbandster.core.worker import Worker
  import logging
label_desc: Use image node_id 4 for calling the PyTorchWorker obj's underlying member method code with example usage: obj.get_configspace() and returns: cs