Source code for optuna.samplers._tpe.multi_objective_sampler

from typing import Callable
from typing import Optional

import numpy as np

from optuna._deprecated import deprecated_class
from optuna.samplers._tpe.sampler import TPESampler

EPS = 1e-12

def default_gamma(x: int) -> int:
    return int(np.floor(0.1 * x))

def _default_weights_above(x: int) -> np.ndarray:
    return np.ones(x)

[docs]@deprecated_class("2.9.0", "4.0.0") class MOTPESampler(TPESampler): """Multi-objective sampler using the MOTPE algorithm. This sampler is a multiobjective version of :class:`~optuna.samplers.TPESampler`. For further information about MOTPE algorithm, please refer to the following paper: - `Multiobjective tree-structured parzen estimator for computationally expensive optimization problems <>`_ - `Multiobjective Tree-Structured Parzen Estimator <>`_ Args: consider_prior: Enhance the stability of Parzen estimator by imposing a Gaussian prior when :obj:`True`. The prior is only effective if the sampling distribution is either :class:`~optuna.distributions.FloatDistribution`, or :class:`~optuna.distributions.IntDistribution`. prior_weight: The weight of the prior. This argument is used in :class:`~optuna.distributions.FloatDistribution`, :class:`~optuna.distributions.IntDistribution`, and :class:`~optuna.distributions.CategoricalDistribution`. 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 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: Number of candidate samples used to calculate the expected hypervolume 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_above: 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: 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: .. testcode:: 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) """ def __init__( self, *, consider_prior: bool = True, prior_weight: float = 1.0, consider_magic_clip: bool = True, consider_endpoints: bool = True, n_startup_trials: int = 10, n_ehvi_candidates: int = 24, gamma: Callable[[int], int] = default_gamma, weights_above: Callable[[int], np.ndarray] = _default_weights_above, seed: Optional[int] = None, ) -> None: super().__init__( consider_prior=consider_prior, prior_weight=prior_weight, consider_magic_clip=consider_magic_clip, consider_endpoints=consider_endpoints, n_startup_trials=n_startup_trials, n_ei_candidates=n_ehvi_candidates, gamma=gamma, weights=weights_above, seed=seed, )