# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
# Copyright 2022 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
r"""Sampling for multidisciplinary design problems under uncertainty.
:class:`~gemseo_umdo.formulations.sampling.Sampling` is an
:class:`~gemseo_umdo.formulations.formulation.UMDOFormulation`
estimating the statistics with (quasi) Monte Carlo techniques.
E.g.
:math:`\mathbb{E}[f(x,U)] \approx \frac{1}{N}\sum_{i=1}^N f\left(x,U^{(i)}\right)`
or
:math:`\mathbb{V}[f(x,U)] \approx
\frac{1}{N}\sum_{i=1}^N \left(f\left(x,U^{(i)}\right)-
\frac{1}{N}\sum_{j=1}^N f\left(x,U^{(j)}\right)\right)^2`
where :math:`U` is normally distributed
with mean :math:`\mu` and unit variance :math:`\sigma`
and :math:`U^{(1)},\ldots,U^{(1)}` are :math:`N` realizations of :math:`U`
obtained with an optimized Latin hypercube sampling technique.
"""
from __future__ import annotations
import logging
from typing import Any
from typing import ClassVar
from typing import Mapping
from typing import Sequence
from gemseo.algos.design_space import DesignSpace
from gemseo.algos.doe.doe_factory import DOEFactory
from gemseo.algos.opt_problem import OptimizationProblem
from gemseo.algos.parameter_space import ParameterSpace
from gemseo.core.discipline import MDODiscipline
from gemseo.core.formulation import MDOFormulation
from gemseo.utils.logging_tools import LoggingContext
from numpy import ndarray
from gemseo_umdo.estimators.sampling import SamplingEstimatorFactory
from gemseo_umdo.formulations.formulation import UMDOFormulation
LOGGER = logging.getLogger(__name__)
[docs]class Sampling(UMDOFormulation):
"""Sampling-based robust MDO formulation."""
processed_functions: list[str]
"""The names of the functions whose statistics have been estimated."""
_STATISTIC_FACTORY: ClassVar = SamplingEstimatorFactory()
def __init__(
self,
disciplines: Sequence[MDODiscipline],
objective_name: str,
design_space: DesignSpace,
mdo_formulation: MDOFormulation,
uncertain_space: ParameterSpace,
objective_statistic_name: str,
n_samples: int,
objective_statistic_parameters: Mapping[str, Any] | None = None,
maximize_objective: bool = False,
grammar_type: str = MDODiscipline.JSON_GRAMMAR_TYPE,
algo: str = "OT_OPT_LHS",
algo_options: Mapping[str, Any] | None = None,
seed: int = 1,
**options: Any,
) -> None:
"""# noqa: D205 D212 D415
Args:
n_samples: The number of samples, i.e. the size of the DOE.
algo: The name of the DOE algorithm.
algo_options: The options of the DOE algorithm.
"""
self.__doe_algo = DOEFactory().create(algo)
self.__doe_algo_options = algo_options or {}
self.__doe_algo_options["n_samples"] = n_samples
self.__n_samples = n_samples
self.processed_functions = []
self.__seed = seed
super().__init__(
disciplines,
objective_name,
design_space,
mdo_formulation,
uncertain_space,
objective_statistic_name,
objective_statistic_options=objective_statistic_parameters,
maximize_objective=maximize_objective,
grammar_type=grammar_type,
**options,
)
mdo_formulation = self._mdo_formulation.__class__.__name__
formulation = self.__class__.__name__
self.name = f"{formulation}[{mdo_formulation}; {algo}({n_samples})]"
@property
def _n_samples(self) -> int:
"""The number of samples."""
return self.__n_samples
@property
def _algo(self) -> str:
"""The name of the DOE algorithm."""
return self.__doe_algo
[docs] def compute_samples(self, problem: OptimizationProblem) -> None:
"""Evaluate the functions of a problem with a DOE algorithm.
Args:
problem: The problem.
"""
with LoggingContext(LOGGER, logging.WARNING):
self.__doe_algo.seed = self.__seed
self.__doe_algo.execute(
problem, seed=self.__seed, **self.__doe_algo_options
)
class _StatisticFunction(UMDOFormulation._StatisticFunction):
def _func(self, input_data: ndarray) -> ndarray:
formulation = self._formulation
problem = formulation.mdo_formulation.opt_problem
if self._function_name in formulation.processed_functions:
formulation.processed_functions = []
problem.reset()
database = problem.database
if not database:
formulation.update_top_level_disciplines(input_data)
formulation.compute_samples(problem)
formulation.processed_functions.append(self._function_name)
samples, _, _ = database.get_history_array(
[self._function_name], add_dv=False
)
return self._estimate_statistic(samples, **self._statistic_parameters)
