(self, configspace = None,
					eta = 3, min_budget=1, max_budget=1,
					**kwargs
					)
Implements a random search across the search space for comparison.
                Candidates are sampled at random and run on the maximum budget.

		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.
		budget : float
			budget for the evaluation
Implements a random search across the search space for comparison.
                Candidates are sampled at random and run on the maximum budget.
Implements a random search across the search space for comparison . Candidates are sampled at random and run on the maximum budget .
def __init__(self, configspace = None,
					eta = 3, min_budget=1, max_budget=1,
					**kwargs
					):
		"""
                Implements a random search across the search space for comparison.
                Candidates are sampled at random and run on the maximum budget.

		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.
		budget : float
			budget for the evaluation
		"""

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



		cg = RS( configspace = configspace )

		super().__init__(config_generator=cg, **kwargs)

		# Hyperband related stuff
		self.eta = eta
		self.min_budget = max_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))

		# max total budget for one iteration
		self.budget_per_iteration = sum([b*self.eta**i for i, b in enumerate(self.budgets[::-1])])
		
		self.config.update({
						'eta'        : eta,
						'min_budget' : max_budget,
						'max_budget' : max_budget,
					})
def __init__ ( self , configspace = None , eta = 3 , min_budget = 1 , max_budget = 1 , * * kwargs ) : # TODO: Propper check for ConfigSpace object! if configspace is None : raise ValueError ( "You have to provide a valid ConfigSpace object" ) cg = RS ( configspace = configspace ) super ( ) . __init__ ( config_generator = cg , * * kwargs ) # Hyperband related stuff self . eta = eta self . min_budget = max_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 ) ) # max total budget for one iteration self . budget_per_iteration = sum ( [ b * self . eta ** i for i , b in enumerate ( self . budgets [ : : - 1 ] ) ] ) self . config . update ( { 'eta' : eta , 'min_budget' : max_budget , 'max_budget' : max_budget , } )