optuna.samplers.NSGAIISampler
- class optuna.samplers.NSGAIISampler(*, population_size=50, mutation_prob=None, crossover=None, crossover_prob=0.9, swapping_prob=0.5, seed=None, constraints_func=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 (int) – Number of individuals (trials) in a generation.
population_size
must be greater than or equal tocrossover.n_parents
. ForUNDXCrossover
andSPXCrossover
,n_parents=3
, and for the other algorithms,n_parents=2
.mutation_prob (Optional[float]) – Probability of mutating each parameter when creating a new individual. If
None
is specified, the value1.0 / len(parent_trial.params)
is used whereparent_trial
is the parent trial of the target individual.crossover (Optional[BaseCrossover]) –
Crossover to be applied when creating child individuals. The available crossovers are listed here: https://optuna.readthedocs.io/en/stable/reference/samplers/nsgaii.html.
UniformCrossover
is always applied to parameters sampled fromCategoricalDistribution
, and by default for parameters sampled from other distributions unless this argument is specified.For more information on each of the crossover method, please refer to specific crossover documentation.
crossover_prob (float) – Probability that a crossover (parameters swapping between parents) will occur when creating a new individual.
swapping_prob (float) – Probability of swapping each parameter of the parents during crossover.
constraints_func (Optional[Callable[[FrozenTrial], Sequence[float]]]) –
An optional function that computes the objective constraints. It must take a
FrozenTrial
and return the constraints. The return value must be a sequence offloat
s. A value strictly larger than 0 means that a constraints is violated. A value equal to or smaller than 0 is considered feasible. Ifconstraints_func
returns more than one value for a trial, that trial is considered feasible if and only if all values are equal to 0 or smaller.The
constraints_func
will be evaluated after each successful trial. The function won’t be called when trials fail or they are pruned, but this behavior is subject to change in the future releases.The constraints are handled by the constrained domination. A trial x is said to constrained-dominate a trial y, if any of the following conditions is true:
Trial x is feasible and trial y is not.
Trial x and y are both infeasible, but trial x has a smaller overall violation.
Trial x and y are feasible and trial x dominates trial y.
Note
Added in v2.5.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.5.0.
Methods
after_trial
(study, trial, state, values)Trial post-processing.
infer_relative_search_space
(study, trial)Infer the search space that will be used by relative sampling in the target trial.
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.
- after_trial(study, trial, state, values)[source]
Trial post-processing.
This method is called after the objective function returns and right before the trials is finished and its state is stored.
Note
Added in v2.4.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.4.0.
- Parameters
study (Study) – Target study object.
trial (FrozenTrial) – Target trial object. Take a copy before modifying this object.
state (TrialState) – Resulting trial state.
values (Optional[Sequence[float]]) – Resulting trial values. Guaranteed to not be
None
if trial succeeded.
- Return type
None
- 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 usingsample_independent()
method.- Parameters
study (Study) – Target study object.
trial (FrozenTrial) – Target trial object. Take a copy before modifying this object.
- Returns
A dictionary containing the parameter names and parameter’s distributions.
- Return type
See also
Please refer to
intersection_search_space()
as an implementation ofinfer_relative_search_space()
.
- reseed_rng()[source]
Reseed sampler’s random number generator.
This method is called by the
Study
instance if trials are executed in parallel with the optionn_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 relationship between parameters such as random sampling and TPE.Note
The failed trials are ignored by any build-in samplers when they sample new parameters. Thus, failed trials are regarded as deleted in the samplers’ perspective.
- Parameters
study (Study) – Target study object.
trial (FrozenTrial) – Target trial object. Take a copy before modifying this object.
param_name (str) – Name of the sampled parameter.
param_distribution (BaseDistribution) – Distribution object that specifies a prior and/or scale of the sampling algorithm.
- Returns
A parameter value.
- Return type
- 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 relationship between parameters such as Gaussian Process and CMA-ES.
Note
The failed trials are ignored by any build-in samplers when they sample new parameters. Thus, failed trials are regarded as deleted in the samplers’ perspective.
- Parameters
study (Study) – Target study object.
trial (FrozenTrial) – Target trial object. Take a copy before modifying this object.
search_space (Dict[str, BaseDistribution]) – The search space returned by
infer_relative_search_space()
.
- Returns
A dictionary containing the parameter names and the values.
- Return type