optuna.samplers.GridSampler

class optuna.samplers.GridSampler(search_space, seed=None)[source]

Sampler using grid search.

With GridSampler, the trials suggest all combinations of parameters in the given search space during the study.

Example

import optuna


def objective(trial):
    x = trial.suggest_float("x", -100, 100)
    y = trial.suggest_int("y", -100, 100)
    return x**2 + y**2


search_space = {"x": [-50, 0, 50], "y": [-99, 0, 99]}
study = optuna.create_study(sampler=optuna.samplers.GridSampler(search_space))
study.optimize(objective)

Note

This sampler with Ask-and-Tell Interface raises RuntimeError just after evaluating the final grid. This is because GridSampler automatically stops the optimization if all combinations in the passed search_space have already been evaluated, internally invoking the stop() method. As a workaround, we need to handle the error manually as in https://github.com/optuna/optuna/issues/4121#issuecomment-1305289910.

Note

GridSampler does not take care of a parameter’s quantization specified by discrete suggest methods but just samples one of values specified in the search space. E.g., in the following code snippet, either of -0.5 or 0.5 is sampled as x instead of an integer point.

import optuna


def objective(trial):
    # The following suggest method specifies integer points between -5 and 5.
    x = trial.suggest_float("x", -5, 5, step=1)
    return x**2


# Non-int points are specified in the grid.
search_space = {"x": [-0.5, 0.5]}
study = optuna.create_study(sampler=optuna.samplers.GridSampler(search_space))
study.optimize(objective, n_trials=2)

Note

A parameter configuration in the grid is not considered finished until its trial is finished. Therefore, during distributed optimization where trials run concurrently, different workers will occasionally suggest the same parameter configuration. The total number of actual trials may therefore exceed the size of the grid.

Note

All parameters must be specified when using GridSampler with enqueue_trial().

Parameters:
  • search_space (Mapping[str, Sequence[None | bool | int | float | str]]) – A dictionary whose key and value are a parameter name and the corresponding candidates of values, respectively.

  • seed (int | None) – A seed to fix the order of trials as the grid is randomly shuffled. Please note that it is not recommended using this option in distributed optimization settings since this option cannot ensure the order of trials and may increase the number of duplicate suggestions during distributed optimization.

Methods

after_trial(study, trial, state, values)

Trial post-processing.

before_trial(study, trial)

Trial pre-processing.

infer_relative_search_space(study, trial)

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

is_exhausted(study)

Return True if all the possible params are evaluated, otherwise return False.

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.

after_trial(study, trial, state, values)[source]

Trial post-processing.

This method is called after the objective function returns and right before the trial 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 (Sequence[float] | None) – Resulting trial values. Guaranteed to not be None if trial succeeded.

Return type:

None

before_trial(study, trial)[source]

Trial pre-processing.

This method is called before the objective function is called and right after the trial is instantiated. More precisely, this method is called during trial initialization, just before the infer_relative_search_space() call. In other words, it is responsible for pre-processing that should be done before inferring the search space.

Note

Added in v3.3.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.3.0.

Parameters:
  • study (Study) – Target study object.

  • trial (FrozenTrial) – Target trial object.

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 using sample_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:

Dict[str, BaseDistribution]

See also

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

is_exhausted(study)[source]

Return True if all the possible params are evaluated, otherwise return False.

Parameters:

study (Study) –

Return type:

bool

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 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 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:

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 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:
Returns:

A dictionary containing the parameter names and the values.

Return type:

Dict[str, Any]