Samplers

class optuna.multi_objective.samplers.BaseMultiObjectiveSampler[source]

Base class for multi-objective samplers.

The abstract methods of this class are the same as ones defined by BaseSampler except for taking multi-objective versions of study and trial instances as the arguments.

Note

Added in v1.4.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v1.4.0.

abstract infer_relative_search_space(study: optuna.multi_objective.study.MultiObjectiveStudy, trial: optuna.multi_objective.trial.FrozenMultiObjectiveTrial) → Dict[str, optuna.distributions.BaseDistribution][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
  • study – Target study object.

  • trial – Target trial object.

Returns

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

See also

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

abstract sample_independent(study: optuna.multi_objective.study.MultiObjectiveStudy, trial: optuna.multi_objective.trial.FrozenMultiObjectiveTrial, param_name: str, param_distribution: optuna.distributions.BaseDistribution) → Any[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
  • study – Target study object.

  • trial – Target trial object.

  • param_name – Name of the sampled parameter.

  • param_distribution – Distribution object that specifies a prior and/or scale of the sampling algorithm.

Returns

A parameter value.

abstract sample_relative(study: optuna.multi_objective.study.MultiObjectiveStudy, trial: optuna.multi_objective.trial.FrozenMultiObjectiveTrial, search_space: Dict[str, optuna.distributions.BaseDistribution]) → Dict[str, Any][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.

class optuna.multi_objective.samplers.NSGAIIMultiObjectiveSampler(population_size: int = 50, mutation_prob: Optional[float] = None, crossover_prob: float = 0.9, swapping_prob: float = 0.5, seed: Optional[int] = None)[source]

Multi-objective sampler using the NSGA-II algorithm.

NSGA-II stands for “Nondominated Sorting Genetic Algorithm II”, which is a well known, fast and elitist multi-objective genetic algorithm.

For further information about NSGA-II, please refer to the following paper:

Parameters
  • population_size – Number of individuals (trials) in a generation.

  • mutation_prob – Probability of mutating each parameter when creating a new individual. If None is specified, the value 1.0 / len(parent_trial.params) is used where parent_trial is the parent trial of the target individual.

  • crossover_prob – Probability that a crossover (parameters swapping between parents) will occur when creating a new individual.

  • swapping_prob – Probability of swapping each parameter of the parents during crossover.

  • seed – Seed for random number generator.

Note

Added in v1.5.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v1.5.0.

class optuna.multi_objective.samplers.RandomMultiObjectiveSampler(seed: Optional[int] = 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.

Note

Added in v1.4.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v1.4.0.