# optuna.samplers.TPESampler¶

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, *, multivariate=False, warn_independent_sampling=True)[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

Methods

 after_trial(study, trial, state, values) Trial post-processing. Return the the default parameters of hyperopt (v0.1.2). 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)

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 (optuna.study.Study) – Target study object.

• trial (optuna.trial._frozen.FrozenTrial) – Target trial object. Take a copy before modifying this object.

• state (optuna.trial._state.TrialState) – Resulting trial state.

• values (Optional[Sequence[float]]) – Resulting trial values. Guaranteed to not be None if trial succeeded.

Return type

None

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.

Return type

Dict[str, Any]

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 (optuna.study.Study) – Target study object.

• trial (optuna.trial._frozen.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, optuna.distributions.BaseDistribution]

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 (optuna.study.Study) – Target study object.

• trial (optuna.trial._frozen.FrozenTrial) – Target trial object. Take a copy before modifying this object.

• param_name (str) – Name of the sampled parameter.

• param_distribution (optuna.distributions.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]