Random Search Algorithm

orion.algo.random – Random sampler as optimization algorithm

class orion.algo.random.Random(space, seed=None)

Implement a algorithm that samples randomly from the problem’s space.

Attributes:

configuration

Return tunable elements of this algorithm in a dictionary form appropriate for saving.

is_done

Return True, if an algorithm holds that there can be no further improvement.

should_suspend

Allow algorithm to decide whether a particular running trial is still worth to complete its evaluation, based on information provided by the judge method.

space

Domain of problem associated with this algorithm’s instance.

state_dict

Return a state dict that can be used to reset the state of the algorithm.

Methods

judge(point, measurements)	Inform an algorithm about online measurements of a running trial.
observe(points, results)	Observe the results of the evaluation of the points in the process defined in user’s script.
score(point)	Allow algorithm to evaluate point based on a prediction about this parameter set’s performance.
seed_rng(seed)	Seed the state of the random number generator.
set_state(state_dict)	Reset the state of the algorithm based on the given state_dict
suggest([num])	Suggest a num of new sets of parameters.

seed_rng(seed)

Seed the state of the random number generator.

Parameters:seed – Integer seed for the random number generator.

set_state(state_dict)

Reset the state of the algorithm based on the given state_dict

Parameters:state_dict – Dictionary representing state of an algorithm

state_dict

Return a state dict that can be used to reset the state of the algorithm.

suggest(num=1)

Suggest a num of new sets of parameters. Randomly draw samples from the import space and return them.

Parameters:num – how many sets to be suggested.

Note

New parameters must be compliant with the problem’s domain orion.algo.space.Space.