Random Search Algorithm¶
orion.algo.random
– Random sampler as optimization algorithm¶
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class
orion.algo.random.
Random
(space, seed=None)[source]¶ 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)[source]¶ Seed the state of the random number generator.
Parameters: seed – Integer seed for the random number generator.
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set_state
(state_dict)[source]¶ Reset the state of the algorithm based on the given state_dict
Parameters: state_dict – Dictionary representing state of an algorithm
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state_dict
¶ Return a state dict that can be used to reset the state of the algorithm.
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suggest
(num=1)[source]¶ 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
.