Random Search¶
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class
olympus.hpo.random_search.RandomSearch(fidelity: olympus.hpo.fidelity.Fidelity, count: int, space: sspace.space.Space, seed=0, pool_size=None, **kwargs)[source]¶ Bases:
olympus.hpo.optimizer.HyperParameterOptimizerRandomly samples sets of hyper parameter and return the best one
Parameters: - count: int
Number of configuration to sample
Notes
Methods
info()Return information about the algo configuration insert_manual_sample([sample, fidelity_override])Can be used to force a specific configuration to be considered is_done()Return true if the optimization is finished new_result(identity, result)Event sent when a new result is received new_trials(trials)Event sent when a new configurations are sampled observe(identity, collections.OrderedDict], …)Observe the result of a given trial remaining()Return the number of remaining trials, this is used to kill superfluous worker if possible result()Return the configuration with the smallest objective sample([count])Sample new configurations and register them suggest(**variables)Return configuration to run count_done from_dict load_state_dict state_dict -
info()[source]¶ Return information about the algo configuration
Returns: - Dictionary, the content is dependant on the algorithm
- unique_samples: int
Number of unique configuration that was sampled
- total_epochs: int
Total number of epochs performed
- parallelism: int
Average number of trial alive at the same time