(self, config_id, config, budget, working_directory)
The function you have to overload implementing your computation.
		
		Parameters
		----------
		config_id: tuple
			a triplet of ints that uniquely identifies a configuration. the convention is
			id = (iteration, budget index, running index) with the following meaning:
			- iteration: the iteration of the optimization algorithms. E.g, for Hyperband that is one round of Successive Halving
			- budget index: the budget (of the current iteration) for which this configuration was sampled by the optimizer. This is only nonzero if the majority of the runs fail and Hyperband resamples to fill empty slots, or you use a more 'advanced' optimizer.
			- running index: this is simply an int >= 0 that sort the configs into the order they where sampled, i.e. (x,x,0) was sampled before (x,x,1).
		config: dict
			the actual configuration to be evaluated.
		budget: float
			the budget for the evaluation
		working_directory: str
			a name of a directory that is unique to this configuration. Use this to store intermediate results on lower budgets that can be reused later for a larger budget (for iterative algorithms, for example).
		Returns
		-------
		dict:
			needs to return a dictionary with two mandatory entries:
				- 'loss': a numerical value that is MINIMIZED
				- 'info': This can be pretty much any build in python type, e.g. a dict with lists as value. Due to Pyro4 handling the remote function calls, 3rd party types like numpy arrays are not supported!
The function you have to overload implementing your computation.
		
		Parameters
		----------
		config_id: tuple
			a triplet of ints that uniquely identifies a configuration. the convention is
			id = (iteration, budget index, running index) with the following meaning:
			- iteration: the iteration of the optimization algorithms. E.g, for Hyperband that is one round of Successive Halving
			- budget index: the budget (of the current iteration) for which this configuration was sampled by the optimizer. This is only nonzero if the majority of the runs fail and Hyperband resamples to fill empty slots, or you use a more 'advanced' optimizer.
			- running index: this is simply an int >= 0 that sort the configs into the order they where sampled, i.e. (x,x,0) was sampled before (x,x,1).
		config: dict
			the actual configuration to be evaluated.
		budget: float
			the budget for the evaluation
		working_directory: str
			a name of a directory that is unique to this configuration. Use this to store intermediate results on lower budgets that can be reused later for a larger budget (for iterative algorithms, for example).
		Returns
		-------
		dict:
			needs to return a dictionary with two mandatory entries:
				- 'loss': a numerical value that is MINIMIZED
				- 'info': This can be pretty much any build in python type, e.g. a dict with lists as value. Due to Pyro4 handling the remote function calls, 3rd party types like numpy arrays are not supported!
The function you have to overload implementing your computation . Parameters ---------- config_id : tuple a triplet of ints that uniquely identifies a configuration . the convention is id = ( iteration budget index running index ) with the following meaning : - iteration : the iteration of the optimization algorithms . E . g for Hyperband that is one round of Successive Halving - budget index : the budget ( of the current iteration ) for which this configuration was sampled by the optimizer . This is only nonzero if the majority of the runs fail and Hyperband resamples to fill empty slots or you use a more advanced optimizer . - running index : this is simply an int > = 0 that sort the configs into the order they where sampled i . e . ( x x 0 ) was sampled before ( x x 1 ) . config : dict the actual configuration to be evaluated . budget : float the budget for the evaluation working_directory : str a name of a directory that is unique to this configuration . Use this to store intermediate results on lower budgets that can be reused later for a larger budget ( for iterative algorithms for example ) . Returns ------- dict : needs to return a dictionary with two mandatory entries : - loss : a numerical value that is MINIMIZED - info : This can be pretty much any build in python type e . g . a dict with lists as value . Due to Pyro4 handling the remote function calls 3rd party types like numpy arrays are not supported!
def compute(self, config_id, config, budget, working_directory):
		""" The function you have to overload implementing your computation.
		
		Parameters
		----------
		config_id: tuple
			a triplet of ints that uniquely identifies a configuration. the convention is
			id = (iteration, budget index, running index) with the following meaning:
			- iteration: the iteration of the optimization algorithms. E.g, for Hyperband that is one round of Successive Halving
			- budget index: the budget (of the current iteration) for which this configuration was sampled by the optimizer. This is only nonzero if the majority of the runs fail and Hyperband resamples to fill empty slots, or you use a more 'advanced' optimizer.
			- running index: this is simply an int >= 0 that sort the configs into the order they where sampled, i.e. (x,x,0) was sampled before (x,x,1).
		config: dict
			the actual configuration to be evaluated.
		budget: float
			the budget for the evaluation
		working_directory: str
			a name of a directory that is unique to this configuration. Use this to store intermediate results on lower budgets that can be reused later for a larger budget (for iterative algorithms, for example).
		Returns
		-------
		dict:
			needs to return a dictionary with two mandatory entries:
				- 'loss': a numerical value that is MINIMIZED
				- 'info': This can be pretty much any build in python type, e.g. a dict with lists as value. Due to Pyro4 handling the remote function calls, 3rd party types like numpy arrays are not supported!
		"""
		
		raise NotImplementedError("Subclass hpbandster.distributed.worker and overwrite the compute method in your worker script")
def compute ( self , config_id , config , budget , working_directory ) : raise NotImplementedError ( "Subclass hpbandster.distributed.worker and overwrite the compute method in your worker script" )