(self, configspace = None,
					eta=3, min_budget=0.01, max_budget=1,
					**kwargs )
Hyperband implements hyperparameter optimization by sampling
                candidates at random and "trying" them first, running them for
                a specific budget. The approach is iterative, promising
                candidates are run for a longer time, increasing the fidelity
                for their performance. While this is a very efficient racing
                approach, random sampling makes no use of the knowledge gained
                about the candidates during optimization.

		Parameters
		----------
		configspace: ConfigSpace object
			valid representation of the search space
		eta : float
			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.
		min_budget : float
			The smallest budget to consider. Needs to be positive!
		max_budget : float
			the largest budget to consider. Needs to be larger than min_budget!
			The budgets will be geometrically distributed $\sim \eta^k$ for
			$k\in [0, 1, ... , num_subsets - 1]$.
Hyperband implements hyperparameter optimization by sampling
                candidates at random and "trying" them first, running them for
                a specific budget. The approach is iterative, promising
                candidates are run for a longer time, increasing the fidelity
                for their performance. While this is a very efficient racing
                approach, random sampling makes no use of the knowledge gained
                about the candidates during optimization.
Hyperband implements hyperparameter optimization by sampling candidates at random and trying them first running them for a specific budget . The approach is iterative promising candidates are run for a longer time increasing the fidelity for their performance . While this is a very efficient racing approach random sampling makes no use of the knowledge gained about the candidates during optimization .
def __init__(self, configspace = None,
					eta=3, min_budget=0.01, max_budget=1,
					**kwargs ):
		"""
                Hyperband implements hyperparameter optimization by sampling
                candidates at random and "trying" them first, running them for
                a specific budget. The approach is iterative, promising
                candidates are run for a longer time, increasing the fidelity
                for their performance. While this is a very efficient racing
                approach, random sampling makes no use of the knowledge gained
                about the candidates during optimization.

		Parameters
		----------
		configspace: ConfigSpace object
			valid representation of the search space
		eta : float
			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.
		min_budget : float
			The smallest budget to consider. Needs to be positive!
		max_budget : float
			the largest budget to consider. Needs to be larger than min_budget!
			The budgets will be geometrically distributed $\sim \eta^k$ for
			$k\in [0, 1, ... , num_subsets - 1]$.
		"""


		# TODO: Propper check for ConfigSpace object!
		if configspace is None:
			raise ValueError("You have to provide a valid CofigSpace object")

		super().__init__(config_generator=RandomSampling(configspace), **kwargs)

		# Hyperband related stuff
		self.eta = eta
		self.min_budget = min_budget
		self.max_budget = max_budget

		# precompute some HB stuff
		self.max_SH_iter = -int(np.log(min_budget/max_budget)/np.log(eta)) + 1
		self.budgets = max_budget * np.power(eta, -np.linspace(self.max_SH_iter-1, 0, self.max_SH_iter))

		self.config.update({
						'eta'        : eta,
						'min_budget' : min_budget,
						'max_budget' : max_budget,
						'budgets'    : self.budgets,
						'max_SH_iter': self.max_SH_iter,
					})
def __init__ ( self , configspace = None , eta = 3 , min_budget = 0.01 , max_budget = 1 , * * kwargs ) : # TODO: Propper check for ConfigSpace object! if configspace is None : raise ValueError ( "You have to provide a valid CofigSpace object" ) super ( ) . __init__ ( config_generator = RandomSampling ( configspace ) , * * kwargs ) # Hyperband related stuff self . eta = eta self . min_budget = min_budget self . max_budget = max_budget # precompute some HB stuff self . max_SH_iter = - int ( np . log ( min_budget / max_budget ) / np . log ( eta ) ) + 1 self . budgets = max_budget * np . power ( eta , - np . linspace ( self . max_SH_iter - 1 , 0 , self . max_SH_iter ) ) self . config . update ( { 'eta' : eta , 'min_budget' : min_budget , 'max_budget' : max_budget , 'budgets' : self . budgets , 'max_SH_iter' : self . max_SH_iter , } )