optuna.samplers.TPESampler

class optuna.samplers.TPESampler(consider_prior: bool = True, prior_weight: float = 1.0, consider_magic_clip: bool = True, consider_endpoints: bool = False, n_startup_trials: int = 10, n_ei_candidates: int = 24, gamma: Callable[[int], int] = <function default_gamma>, weights: Callable[[int], numpy.ndarray] = <function default_weights>, seed: Optional[int] = 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
__init__(consider_prior: bool = True, prior_weight: float = 1.0, consider_magic_clip: bool = True, consider_endpoints: bool = False, n_startup_trials: int = 10, n_ei_candidates: int = 24, gamma: Callable[[int], int] = <function default_gamma>, weights: Callable[[int], numpy.ndarray] = <function default_weights>, seed: Optional[int] = None)None[source]

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

Methods

__init__([consider_prior, prior_weight, …])

Initialize self.

hyperopt_parameters()

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_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.

static hyperopt_parameters() → Dict[str, Any][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.

infer_relative_search_space(study: optuna.study.Study, trial: optuna.trial._frozen.FrozenTrial) → Dict[str, optuna.distributions.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.

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.

sample_independent(study: optuna.study.Study, trial: optuna.trial._frozen.FrozenTrial, param_name: str, param_distribution: optuna.distributions.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: optuna.study.Study, trial: optuna.trial._frozen.FrozenTrial, search_space: Dict[str, optuna.distributions.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
  • 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.