optuna.samplers.QMCSampler

class optuna.samplers.QMCSampler(*, qmc_type='sobol', scramble=False, seed=None, independent_sampler=None, warn_asynchronous_seeding=True, warn_independent_sampling=True)[source]

A Quasi Monte Carlo Sampler that generates low-discrepancy sequences.

Quasi Monte Carlo (QMC) sequences are designed to have lower discrepancies than standard random seqeunces. They are known to perform better than the standard randam sequences in hyperparameter optimization.

For further information about the use of QMC sequences for hyperparameter optimization, please refer to the following paper:

Bergstra, James, and Yoshua Bengio. Random search for hyper-parameter optimization. Journal of machine learning research 13.2, 2012.

We use the QMC implementations in Scipy. For the details of the QMC algorithm, see the Scipy API references on scipy.stats.qmc.

Note

The search space of the sampler is determined by either previous trials in the study or the first trial that this sampler samples.

If there are previous trials in the study, QMCSamper infers its search space using the trial which was created first in the study.

Otherwise (if the study has no previous trials), QMCSampler samples the first trial using its independent_sampler and then infers the search space in the second trial.

As mentioned above, the search space of the QMCSampler is determined by the first trial of the study. Once the search space is determined, it cannot be changed afterwards.

Parameters

qmc_type (str) –
The type of QMC sequence to be sampled. This must be one of “halton” and “sobol”. Default is “sobol”.

Note

Sobol’ sequence is designed to have low-discrepancy property when the number of samples is \(n=2^m\) for each positive integer \(m\). When it is possible to pre-specify the number of trials suggested by QMCSampler, it is recommended that the number of trials should be set as power of two.
scramble (bool) – If this option is True, scrambling (randomization) is applied to the QMC sequences.
seed (Optional[int]) –
A seed for QMCSampler. This argument is used only when scramble is True. If this is None, the seed is initialized randomly. Default is None.

Note

When using multiple QMCSampler’s in parallel and/or distributed optimization, all the samplers must share the same seed when the scrambling is enabled. Otherwise, the low-discrepancy property of the samples will be degraded.
independent_sampler (Optional[BaseSampler]) –
A BaseSampler instance that is used for independent sampling. The first trial of the study and the parameters not contained in the relative search space are sampled by this sampler.

If None is specified, RandomSampler is used as the default.

See also

samplers module provides built-in independent samplers such as RandomSampler and TPESampler.
warn_independent_sampling (bool) –
If this is True, a warning message is emitted when the value of a parameter is sampled by using an independent sampler.

Note that the parameters of the first trial in a study are sampled via an independent sampler in most cases, so no warning messages are emitted in such cases.
warn_asynchronous_seeding (bool) –
If this is True, a warning message is emitted when the scrambling (randomization) is applied to the QMC sequence and the random seed of the sampler is not set manually.

Note

When using parallel and/or distributed optimization without manually setting the seed, the seed is set randomly for each instances of QMCSampler for different workers, which ends up asynchronous seeding for multiple samplers used in the optimization.

See also

See parameter seed in QMCSampler.

Example

Optimize a simple quadratic function by using QMCSampler.

import optuna


def objective(trial):
    x = trial.suggest_float("x", -1, 1)
    y = trial.suggest_int("y", -1, 1)
    return x**2 + y


sampler = optuna.samplers.QMCSampler()
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=8)

Note

Added in v3.0.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.0.0.

Methods

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

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]