Source code for gemseo_umdo.formulations.sampling

# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
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r"""Sampling for multidisciplinary design problems under uncertainty.

[Sampling][gemseo_umdo.formulations.sampling.Sampling] is an
[UMDOFormulation][gemseo_umdo.formulations.formulation.UMDOFormulation]
estimating the statistics with (quasi) Monte Carlo techniques.

E.g.
$\mathbb{E}[f(x,U)] \approx \frac{1}{N}\sum_{i=1}^N f\left(x,U^{(i)}\right)$
or
$\mathbb{V}[f(x,U)] \approx
\frac{1}{N}\sum_{i=1}^N \left(f\left(x,U^{(i)}\right)-
\frac{1}{N}\sum_{j=1}^N f\left(x,U^{(j)}\right)\right)^2$
where $U$ is normally distributed
with mean $\mu$ and variance $\sigma^2$
and $U^{(1)},\ldots,U^{(N)}$ are $N$ realizations of $U$
obtained with an optimized Latin hypercube sampling technique.
"""
from __future__ import annotations

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.doe.doe_library import DOELibrary
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


[docs]class Sampling(UMDOFormulation): """Sampling-based robust MDO formulation.""" _STATISTIC_FACTORY: ClassVar[SamplingEstimatorFactory] = 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: MDODiscipline.GrammarType = MDODiscipline.GrammarType.JSON, algo: str = "OT_OPT_LHS", algo_options: Mapping[str, Any] | None = None, seed: int = 1, **options: Any, ) -> None: """ 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. """ # noqa: D205 D212 D415 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.__doe_algo_options["n_samples"] @_n_samples.setter def _n_samples(self, value: int) -> None: self.__doe_algo_options["n_samples"] = value @property def _algo(self) -> DOELibrary: """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(): 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], with_x_vect=False ) return self._estimate_statistic(samples, **self._statistic_parameters)