# optuna.samplers.GridSampler¶

class optuna.samplers.GridSampler(search_space: Mapping[str, Sequence[GridValueType]])[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_uniform('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, n_trials=3*3)


Note

GridSampler automatically stops the optimization if all combinations in the passed search_space have already been evaluated, internally invoking the stop() method.

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_discrete_uniform('x', -5, 5, 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)

Parameters

search_space – A dictionary whose key and value are a parameter name and the corresponding candidates of values, respectively.

__init__(search_space: Mapping[str, Sequence[GridValueType]])None[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__(search_space) Initialize self. 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.
infer_relative_search_space(study: Study, trial: FrozenTrial) → Dict[str, 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 pass 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. Take a copy before modifying this object.

Returns

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

reseed_rng()None

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.

sample_independent(study: Study, trial: FrozenTrial, param_name: str, param_distribution: 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 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 – Target study object.

• trial – Target trial object. Take a copy before modifying this 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.

sample_relative(study: Study, trial: FrozenTrial, search_space: Dict[str, 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 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.