from typing import Callable
from typing import Optional
import numpy as np
from optuna._deprecated import deprecated
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("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 <https://dl.acm.org/doi/abs/10.1145/3377930.3389817>`_
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,
)