optuna.samplers.MOTPESampler
- class optuna.samplers.MOTPESampler(*, consider_prior=True, prior_weight=1.0, consider_magic_clip=True, consider_endpoints=True, n_startup_trials=10, n_ehvi_candidates=24, gamma=<function default_gamma>, weights_above=<function _default_weights_above>, seed=None)[source]
Multi-objective sampler using the MOTPE algorithm.
This sampler is a multiobjective version of
TPESampler
.For further information about MOTPE algorithm, please refer to the following paper:
- 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 eitherFloatDistribution
, orIntDistribution
.prior_weight (float) – The weight of the prior. This argument is used in
FloatDistribution
,IntDistribution
, andCategoricalDistribution
.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 MOTPE algorithm until the given number of trials finish in the same study. 11 * number of variables - 1 is recommended in the original paper.
n_ehvi_candidates (int) – Number of candidate samples used to calculate the expected hypervolume 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_above (Callable[[int], ndarray]) – A function that takes the number of finished trials and returns a weight for them. As default, weights are automatically calculated by the MOTPE’s default strategy.
seed (int | None) – Seed for random number generator.
Note
Initialization with Latin hypercube sampling may improve optimization performance. However, the current implementation only supports initialization with random sampling.
Example
import optuna seed = 128 num_variables = 2 n_startup_trials = 11 * num_variables - 1 def objective(trial): x = [] for i in range(1, num_variables + 1): x.append(trial.suggest_float(f"x{i}", 0.0, 2.0 * i)) return x sampler = optuna.samplers.MOTPESampler( n_startup_trials=n_startup_trials, n_ehvi_candidates=24, seed=seed ) study = optuna.create_study(directions=["minimize"] * num_variables, sampler=sampler) study.optimize(objective, n_trials=n_startup_trials + 10)
Warning
Deprecated in v2.9.0. This feature will be removed in the future. The removal of this feature is currently scheduled for v4.0.0, but this schedule is subject to change. See https://github.com/optuna/optuna/releases/tag/v2.9.0.
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 trial 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 (Sequence[float] | None) – Resulting trial values. Guaranteed to not be
None
if trial succeeded.
- Return type:
None
- static hyperopt_parameters()
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)
- infer_relative_search_space(study, trial)
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 usingsample_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:
See also
Please refer to
intersection_search_space()
as an implementation ofinfer_relative_search_space()
.
- reseed_rng()
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.- Return type:
None
- sample_independent(study, trial, param_name, param_distribution)
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 (Study) – Target study object.
trial (FrozenTrial) – Target trial object. Take a copy before modifying this object.
param_name (str) – Name of the sampled parameter.
param_distribution (BaseDistribution) – Distribution object that specifies a prior and/or scale of the sampling algorithm.
- Returns:
A parameter value.
- Return type:
- sample_relative(study, trial, search_space)
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 (Study) – Target study object.
trial (FrozenTrial) – Target trial object. Take a copy before modifying this object.
search_space (Dict[str, BaseDistribution]) – The search space returned by
infer_relative_search_space()
.
- Returns:
A dictionary containing the parameter names and the values.
- Return type: