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_float("x", -10, 10)
    return x ** 2


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

Parameters

consider_prior (bool) – 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 (float) – The weight of the prior. This argument is used in UniformDistribution, DiscreteUniformDistribution, LogUniformDistribution, IntUniformDistribution, IntLogUniformDistribution, and CategoricalDistribution.
consider_magic_clip (bool) – Enable a heuristic to limit the smallest variances of Gaussians used in the Parzen estimator.
consider_endpoints (bool) – 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 (int) – The random sampling is used instead of the TPE algorithm until the given number of trials finish in the same study.
n_ei_candidates (int) – Number of candidate samples used to calculate the expected improvement.
gamma (Callable[[int], int]) – 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 (Callable[[int], ndarray]) –
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 (Optional[int]) – Seed for random number generator.
multivariate (bool) –
If this is True, the multivariate TPE is used when suggesting parameters. The multivariate TPE is reported to outperform the independent TPE. See BOHB: Robust and Efficient Hyperparameter Optimization at Scale for more details.

Note

Added in v2.2.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.2.0.
warn_independent_sampling (bool) – If this is True and multivariate=True, a warning message is emitted when the value of a parameter is sampled by using an independent sampler. If multivariate=False, this flag has no effect.

Methods

`after_trial`(study, trial, state, values)	Trial post-processing.
`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.

after_trial(study, trial, state, values)[source]

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 (Study) – Target study object.
trial (FrozenTrial) – Target trial object. Take a copy before modifying this object.
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_float("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 (Study) – Target study object.
trial (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, BaseDistribution]