optuna.multi_objective.samplers.RandomMultiObjectiveSampler

class optuna.multi_objective.samplers.RandomMultiObjectiveSampler(seed=None)[source]

Multi-objective sampler using random sampling.

This sampler is based on independent sampling. See also BaseMultiObjectiveSampler for more details of ‘independent sampling’.

Example

import optuna
from optuna.multi_objective.samplers import RandomMultiObjectiveSampler


def objective(trial):
    x = trial.suggest_uniform("x", -5, 5)
    y = trial.suggest_uniform("y", -5, 5)
    return x ** 2, y + 10


study = optuna.multi_objective.create_study(
    ["minimize", "minimize"], sampler=RandomMultiObjectiveSampler()
)
study.optimize(objective, n_trials=10)
Args:

seed: Seed for random number generator.

Warning

Deprecated in v2.4.0. This feature will be removed in the future. The removal of this feature is currently scheduled for v4.0.0, but this schedule is subject to change. See https://github.com/optuna/optuna/releases/tag/v2.4.0.

Methods

infer_relative_search_space(study, trial)

Infer the search space that will be used by relative sampling in the target trial.

reseed_rng()

Reseed sampler’s random number generator.

sample_independent(study, trial, param_name, …)

Sample a parameter for a given distribution.

sample_relative(study, trial, search_space)

Sample parameters in a given search space.

infer_relative_search_space(study, trial)[source]

Infer the search space that will be used by relative sampling in the target trial.

This method is called right before sample_relative() method, and the search space returned by this method is passed to it. The parameters not contained in the search space will be sampled by using sample_independent() method.

Parameters
Returns

A dictionary containing the parameter names and parameter’s distributions.

Return type

Dict[str, optuna.distributions.BaseDistribution]

See also

Please refer to intersection_search_space() as an implementation of infer_relative_search_space().

reseed_rng()[source]

Reseed sampler’s random number generator.

This method is called by the MultiObjectiveStudy instance if trials are executed in parallel with the option n_jobs>1. In that case, the sampler instance will be replicated including the state of the random number generator, and they may suggest the same values. To prevent this issue, this method assigns a different seed to each random number generator.

Return type

None

sample_independent(study, trial, param_name, param_distribution)[source]

Sample a parameter for a given distribution.

This method is called only for the parameters not contained in the search space returned by sample_relative() method. This method is suitable for sampling algorithms that do not use the relationship between parameters such as random sampling.

Parameters
Returns

A parameter value.

Return type

Any

sample_relative(study, trial, search_space)[source]

Sample parameters in a given search space.

This method is called once at the beginning of each trial, i.e., right before the evaluation of the objective function. This method is suitable for sampling algorithms that use the relationship between parameters.

Parameters
Returns

A dictionary containing the parameter names and the values.

Return type

Dict[str, Any]