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 (Treestructured 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 GMMg(x)
to the remaining parameter values. It chooses the parameter valuex
that maximizes the ratiol(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 eitherUniformDistribution
,DiscreteUniformDistribution
,LogUniformDistribution
,IntUniformDistribution
, orIntLogUniformDistribution
.prior_weight – The weight of the prior. This argument is used in
UniformDistribution
,DiscreteUniformDistribution
,LogUniformDistribution
,IntUniformDistribution
,IntLogUniformDistribution
, andCategoricalDistribution
.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.

__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.
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.

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 usingsample_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 ofinfer_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 optionn_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 buildin 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 CMAES.
Note
The failed trials are ignored by any buildin 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.