Source code for gemseo.formulations.bilevel_test_helper

# Copyright 2021 IRT Saint Exupéry,
# 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
# 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.
"""Provide base test class stub for testing bilevel also for |g| plugins."""

from __future__ import annotations

from copy import deepcopy
from typing import Callable

from gemseo.core.mdo_scenario import MDOScenario
from gemseo.problems.sobieski.disciplines import SobieskiAerodynamics
from gemseo.problems.sobieski.disciplines import SobieskiMission
from gemseo.problems.sobieski.disciplines import SobieskiProblem
from gemseo.problems.sobieski.disciplines import SobieskiPropulsion
from gemseo.problems.sobieski.disciplines import SobieskiStructure

[docs] def create_sobieski_bilevel_scenario( scenario_formulation: str = "BiLevel", ) -> Callable[[dict[str, float]], MDOScenario]: """Create a function to generate a Sobieski Scenario. Args: scenario_formulation: The name of the formulation of the scenario. Returns: A function which generates a Sobieski scenario with specific options. """ def func(**options): """Create a Sobieski BiLevel scenario. Args: **options: The options of the system scenario. Returns: A Sobieski BiLevel Scenario. """ propulsion = SobieskiPropulsion() aerodynamics = SobieskiAerodynamics() struct = SobieskiStructure() mission = SobieskiMission() ds = SobieskiProblem().design_space sc_prop = MDOScenario( disciplines=[propulsion], formulation="DisciplinaryOpt", objective_name="y_34", design_space=deepcopy(ds).filter("x_3"), name="PropulsionScenario", ) # Maximize L/D sc_aero = MDOScenario( disciplines=[aerodynamics], formulation="DisciplinaryOpt", objective_name="y_24", design_space=deepcopy(ds).filter("x_2"), name="AerodynamicsScenario", maximize_objective=True, ) # Maximize log(aircraft total weight / (aircraft total weight - fuel # weight)) sc_str = MDOScenario( disciplines=[struct], formulation="DisciplinaryOpt", objective_name="y_11", design_space=deepcopy(ds).filter("x_1"), name="StructureScenario", maximize_objective=True, ) sub_scenarios = [sc_str, sc_aero, sc_prop] sub_disciplines = [*sub_scenarios, mission] for sc in sub_scenarios: sc.default_inputs = {"max_iter": 5, "algo": "SLSQP"} ds = SobieskiProblem().design_space sc_system = MDOScenario( sub_disciplines, formulation=scenario_formulation, objective_name="y_4", design_space=ds.filter(["x_shared", "y_14"]), maximize_objective=True, **options, ) sc_system.set_differentiation_method("finite_differences") return sc_system return func