optuna.samplers¶

class optuna.samplers.BaseSampler[source]¶

Base class for samplers.

Optuna combines two types of sampling strategies, which are called relative sampling and independent sampling.

The relative sampling determines values of multiple parameters simultaneously so that sampling algorithms can use relationship between parameters (e.g., correlation). Target parameters of the relative sampling are described in a relative search space, which is determined by infer_relative_search_space().

The independent sampling determines a value of a single parameter without considering any relationship between parameters. Target parameters of the independent sampling are the parameters not described in the relative search space.

More specifically, parameters are sampled by the following procedure. At the beginning of a trial, infer_relative_search_space() is called to determine the relative search space for the trial. Then, sample_relative() is invoked to sample parameters from the relative search space. During the execution of the objective function, sample_independent() is used to sample parameters that don’t belong to the relative search space.

The following figure depicts the lifetime of a trial and how the above three methods are called in the trial.

abstract 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 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.

See also

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

reseed_rng() → None [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.

abstract 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 – 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.

abstract 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 – Target study object.
trial – Target trial object. Take a copy before modifying this object.
search_space – The search space returned by infer_relative_search_space().

Returns

A dictionary containing the parameter names and the values.

class optuna.samplers.GridSampler(search_space)[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 raises an error if all combinations in the passed search_space has already been evaluated. Please make sure that unnecessary trials do not run during optimization by properly setting n_trials in the optimize() 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.

Note

Added in v1.2.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v1.2.0.

class optuna.samplers.RandomSampler(seed=None)[source]¶

Sampler using random sampling.

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

Example

import optuna
from optuna.samplers import RandomSampler

def objective(trial):
    x = trial.suggest_uniform('x', -5, 5)
    return x**2

study = optuna.create_study(sampler=RandomSampler())
study.optimize(objective, n_trials=10)

Args:: seed: Seed for random number generator.

reseed_rng() → None [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.

class optuna.samplers.TPESampler(consider_prior=True, prior_weight=1.0, consider_magic_clip=True, consider_endpoints=False, n_startup_trials=10, n_ei_candidates=24, gamma=<function default_gamma>, weights=<function default_weights>, seed=None)[source]¶

Sampler using TPE (Tree-structured Parzen Estimator) algorithm.

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

On each trial, for each parameter, TPE fits one Gaussian Mixture Model (GMM) l(x) to the set of parameter values associated with the best objective values, and another GMM g(x) to the remaining parameter values. It chooses the parameter value x that maximizes the ratio l(x)/g(x).

For further information about TPE algorithm, please refer to the following papers:

Example

import optuna
from optuna.samplers import TPESampler

def objective(trial):
    x = trial.suggest_uniform('x', -10, 10)
    return x**2

study = optuna.create_study(sampler=TPESampler())
study.optimize(objective, n_trials=10)

Parameters

consider_prior – Enhance the stability of Parzen estimator by imposing a Gaussian prior when True. The prior is only effective if the sampling distribution is either UniformDistribution, DiscreteUniformDistribution, LogUniformDistribution, IntUniformDistribution, or IntLogUniformDistribution.
prior_weight – The weight of the prior. This argument is used in UniformDistribution, DiscreteUniformDistribution, LogUniformDistribution, IntUniformDistribution, IntLogUniformDistribution, and CategoricalDistribution.
consider_magic_clip – Enable a heuristic to limit the smallest variances of Gaussians used in the Parzen estimator.
consider_endpoints – Take endpoints of domains into account when calculating variances of Gaussians in Parzen estimator. See the original paper for details on the heuristics to calculate the variances.
n_startup_trials – The random sampling is used instead of the TPE algorithm until the given number of trials finish in the same study.
n_ei_candidates – Number of candidate samples used to calculate the expected improvement.
gamma – A function that takes the number of finished trials and returns the number of trials to form a density function for samples with low grains. See the original paper for more details.
weights –
A function that takes the number of finished trials and returns a weight for them. See Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures for more details.
seed – Seed for random number generator.

static hyperopt_parameters()[source]¶

Return the the default parameters of hyperopt (v0.1.2).

TPESampler can be instantiated with the parameters returned by this method.

Example

Create a TPESampler instance with the default parameters of hyperopt.

import optuna
from optuna.samplers import TPESampler

def objective(trial):
    x = trial.suggest_uniform('x', -10, 10)
    return x**2

sampler = TPESampler(**TPESampler.hyperopt_parameters())
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=10)

Returns: A dictionary containing the default parameters of hyperopt.

reseed_rng() → None [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.

class optuna.samplers.CmaEsSampler(x0: Optional[Dict[str, Any]] = None, sigma0: Optional[float] = None, n_startup_trials: int = 1, independent_sampler: Optional[optuna.samplers._base.BaseSampler] = None, warn_independent_sampling: bool = True, seed: Optional[int] = None, *, consider_pruned_trials: bool = False)[source]¶

A Sampler using CMA-ES algorithm.

Example

Optimize a simple quadratic function by using CmaEsSampler.

import optuna

def objective(trial):
    x = trial.suggest_uniform('x', -1, 1)
    y = trial.suggest_int('y', -1, 1)
    return x ** 2 + y

sampler = optuna.samplers.CmaEsSampler()
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=20)

Please note that this sampler does not support CategoricalDistribution. If your search space contains categorical parameters, I recommend you to use TPESampler instead. Furthermore, there is room for performance improvements in parallel optimization settings. This sampler cannot use some trials for updating the parameters of multivariate normal distribution.