Source code for gemseo.formulations.bilevel

# 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.
# Contributors:
#    INITIAL AUTHORS - API and implementation and/or documentation
#        :author: Francois Gallard
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""A Bi-level formulation."""

from __future__ import annotations

import logging
from typing import TYPE_CHECKING
from typing import Any
from typing import ClassVar

from gemseo.core.chain import MDOChain
from gemseo.core.chain import MDOParallelChain
from gemseo.core.chain import MDOWarmStartedChain
from gemseo.core.coupling_structure import MDOCouplingStructure
from gemseo.core.discipline import MDODiscipline
from gemseo.core.formulation import MDOFormulation
from gemseo.core.mdofunctions.mdo_function import MDOFunction
from gemseo.disciplines.scenario_adapters.mdo_scenario_adapter import MDOScenarioAdapter
from gemseo.mda.mda_factory import MDAFactory
from gemseo.scenarios.scenario_results.bilevel_scenario_result import (
    BiLevelScenarioResult,
)

if TYPE_CHECKING:
    from collections.abc import Iterable
    from collections.abc import Mapping

    from gemseo.algos.design_space import DesignSpace
    from gemseo.core.execution_sequence import ExecutionSequence
    from gemseo.core.grammars.json_grammar import JSONGrammar
    from gemseo.core.scenario import Scenario
    from gemseo.mda.base_mda import BaseMDA

LOGGER = logging.getLogger(__name__)


[docs] class BiLevel(MDOFormulation): """A bi-level formulation. This formulation draws an optimization architecture that involves multiple optimization problems to be solved to obtain the solution of the MDO problem. Here, at each iteration on the global design variables, the bi-level MDO formulation implementation performs: 1. a first MDA to compute the coupling variables, 2. several disciplinary optimizations on the local design variables in parallel, 3. a second MDA to update the coupling variables. """ DEFAULT_SCENARIO_RESULT_CLASS_NAME: ClassVar[str] = BiLevelScenarioResult.__name__ SYSTEM_LEVEL = "system" SUBSCENARIOS_LEVEL = "sub-scenarios" LEVELS = (SYSTEM_LEVEL, SUBSCENARIOS_LEVEL) __sub_scenarios_log_level: int | None """The level of the root logger during the sub-scenarios executions. If ``None``, do not change the level of the root logger. """ def __init__( self, disciplines: list[MDODiscipline], objective_name: str, design_space: DesignSpace, maximize_objective: bool = False, main_mda_name: str = "MDAChain", inner_mda_name: str = "MDAJacobi", parallel_scenarios: bool = False, multithread_scenarios: bool = True, apply_cstr_tosub_scenarios: bool = True, apply_cstr_to_system: bool = True, reset_x0_before_opt: bool = False, grammar_type: MDODiscipline.GrammarType = MDODiscipline.GrammarType.JSON, sub_scenarios_log_level: int | None = None, **main_mda_options: Any, ) -> None: """ Args: main_mda_name: The name of the class used for the main MDA, typically the :class:`.MDAChain`, but one can force to use :class:`.MDAGaussSeidel` for instance. inner_mda_name: The name of the class used for the inner-MDA of the main MDA, if any; typically when the main MDA is an :class:`.MDAChain`. parallel_scenarios: Whether to run the sub-scenarios in parallel. multithread_scenarios: If ``True`` and parallel_scenarios=True, the sub-scenarios are run in parallel using multi-threading; if False and parallel_scenarios=True, multiprocessing is used. apply_cstr_tosub_scenarios: Whether the :meth:`.add_constraint` method adds the constraint to the optimization problem of the sub-scenario capable of computing the constraint. apply_cstr_to_system: Whether the :meth:`.add_constraint` method adds the constraint to the optimization problem of the system scenario. reset_x0_before_opt: Whether to restart the sub optimizations from the initial guesses, otherwise warm start them. sub_scenarios_log_level: The level of the root logger during the sub-scenarios executions. If ``None``, do not change the level of the root logger. **main_mda_options: The options of the main MDA, which may include those of the inner-MDA. """ # noqa: D205, D212, D415 super().__init__( disciplines, objective_name, design_space, maximize_objective=maximize_objective, grammar_type=grammar_type, ) self._shared_dv = list(design_space.variable_names) self._mda1 = None self._mda2 = None self.reset_x0_before_opt = reset_x0_before_opt self.scenario_adapters = [] self.chain = None self._mda_factory = MDAFactory() self._apply_cstr_to_system = apply_cstr_to_system self._apply_cstr_tosub_scenarios = apply_cstr_tosub_scenarios self.__parallel_scenarios = parallel_scenarios self._multithread_scenarios = multithread_scenarios self.couplstr = MDOCouplingStructure(self.get_sub_disciplines()) # Create MDA self.__sub_scenarios_log_level = sub_scenarios_log_level self._build_mdas(main_mda_name, inner_mda_name, **main_mda_options) # Create MDOChain : MDA1 -> sub scenarios -> MDA2 self._build_chain() # Cleanup design space self._update_design_space() # Builds the objective function on top of the chain self._build_objective_from_disc(self._objective_name) @property def mda1(self) -> MDODiscipline: """The MDA1 instance.""" return self._mda1 @property def mda2(self) -> MDODiscipline: """The MDA2 instance.""" return self._mda2 def _build_scenario_adapters( self, output_functions: bool = False, use_non_shared_vars: bool = False, adapter_class: type[MDOScenarioAdapter] = MDOScenarioAdapter, **adapter_options, ) -> list[MDOScenarioAdapter]: """Build the MDOScenarioAdapter required for each sub scenario. This is used to build the self.chain. Args: output_functions: Whether to add the optimization functions in the adapter outputs. use_non_shared_vars: Whether the non-shared design variables are inputs of the scenarios adapters. adapter_class: The class of the adapters. **adapter_options: The options for the adapters' initialization. Returns: The adapters for the sub-scenarios. """ adapters = [] scenario_log_level = adapter_options.pop( "scenario_log_level", self.__sub_scenarios_log_level ) for scenario in self.get_sub_scenarios(): adapter_inputs = self._compute_adapter_inputs(scenario, use_non_shared_vars) adapter_outputs = self._compute_adapter_outputs(scenario, output_functions) adapter = adapter_class( scenario, adapter_inputs, adapter_outputs, grammar_type=self._grammar_type, scenario_log_level=scenario_log_level, **adapter_options, ) adapters.append(adapter) return adapters def _compute_adapter_outputs( self, scenario: Scenario, output_functions: bool, ) -> list[str]: """Compute the scenario adapter outputs. Args: scenario: A sub-scenario. output_functions: Whether to add the objective and constraints in the outputs. Returns: The output variables of the adapter. """ couplings = self.couplstr.all_couplings mda2_inputs = self._get_mda2_inputs() top_disc = scenario.formulation.get_top_level_disc() top_outputs = [ outpt for disc in top_disc for outpt in disc.get_output_data_names() ] # Output couplings of scenario are given to MDA for speedup if output_functions: opt_problem = scenario.formulation.opt_problem sc_output_names = opt_problem.objective.output_names sc_constraints = opt_problem.get_constraint_names() sc_out_coupl = sc_output_names + sc_constraints else: sc_out_coupl = list(set(top_outputs) & set(couplings + mda2_inputs)) # Add private variables from disciplinary scenario design space return sc_out_coupl + scenario.design_space.variable_names def _compute_adapter_inputs( self, scenario: Scenario, use_non_shared_vars: bool, ) -> list[str]: """Compute the scenario adapter inputs. Args: scenario: A sub-scenario. use_non_shared_vars: Whether to add the non-shared variables as inputs of the adapter. Returns: The input variables of the adapter. """ shared_dv = set(self._shared_dv) couplings = self.couplstr.all_couplings mda1_outputs = self._get_mda1_outputs() top_disc = scenario.formulation.get_top_level_disc() top_inputs = [inpt for disc in top_disc for inpt in disc.get_input_data_names()] # All couplings of the scenarios are taken from the MDA adapter_inputs = list( # Add shared variables from system scenario driver set(top_inputs) & (set(couplings) | shared_dv | set(mda1_outputs)) ) if use_non_shared_vars: nonshared_var = scenario.design_space.variable_names adapter_inputs = list( set(adapter_inputs) | set(top_inputs) & set(nonshared_var) ) return adapter_inputs def _get_mda1_outputs(self) -> list[str]: """Return the MDA1 outputs. Returns: The MDA1 outputs. """ return list(self._mda1.get_output_data_names()) if self._mda1 else [] def _get_mda2_inputs(self) -> list[str]: """Return the MDA2 inputs. Returns: The MDA2 inputs. """ return list(self._mda2.get_input_data_names()) if self._mda2 else []
[docs] @classmethod def get_sub_options_grammar(cls, **options: str) -> JSONGrammar: """Return the grammar of the selected MDA. Args: **options: The options of the BiLevel formulation. Returns: The MDA grammar. Raises: ValueError: When the MDA name is not provided. """ main_mda_name = options.get("main_mda_name") if main_mda_name is None: msg = ( "'main_mda_name' option is required to deduce the " "sub options of BiLevel." ) raise ValueError(msg) return MDAFactory().get_options_grammar(main_mda_name)
[docs] @classmethod def get_default_sub_option_values( cls, **options: str ) -> Mapping[str, str | int | float | bool | None] | None: """ Raises: ValueError: When the MDA name is not provided. """ # noqa: D205, D212, D415 main_mda_name = options.get("main_mda_name") if main_mda_name is None: msg = ( "'main_mda_name' option is required to deduce the " "sub options of BiLevel." ) raise ValueError(msg) return MDAFactory().get_default_option_values(main_mda_name)
def _build_mdas( self, main_mda_name: str, inner_mda_name: str, **main_mda_options: str | int | float | bool | None, ) -> None: """Build the chain on top of which all functions are built. This chain is as follows: MDA1 -> MDOScenarios -> MDA2. Args: main_mda_name: The class name of the main MDA. inner_mda_name: The name of the class used for the inner-MDA of the main MDA, if any; typically when the main MDA is an :class:`.MDAChain`. **main_mda_options: The options of the main MDA, which may include those of the inner-MDA. """ if main_mda_name == "MDAChain": main_mda_options["inner_mda_name"] = inner_mda_name disc_mda1 = self.couplstr.strongly_coupled_disciplines if len(disc_mda1) > 0: self._mda1 = self._mda_factory.create( main_mda_name, disc_mda1, grammar_type=self._grammar_type, **main_mda_options, ) self._mda1.warm_start = True else: LOGGER.warning( "No strongly coupled disciplines detected, " " MDA1 is deactivated in the BiLevel formulation" ) disc_mda2 = self.get_sub_disciplines() self._mda2 = self._mda_factory.create( main_mda_name, disc_mda2, grammar_type=self._grammar_type, **main_mda_options, ) self._mda2.warm_start = False def _build_chain_dis_sub_opts( self, ) -> tuple[list | BaseMDA, list[MDOScenarioAdapter]]: """Initialize the chain of disciplines and the sub-scenarios. Returns: The first MDA (if exists) and the sub-scenarios. """ chain_dis = [] if self._mda1 is not None: chain_dis = [self._mda1] sub_opts = self.scenario_adapters return chain_dis, sub_opts def _build_chain(self) -> None: """Build the chain on top of which all functions are built. This chain is: MDA -> MDOScenarios -> MDA. """ # Build the scenario adapters to be chained with MDAs self.scenario_adapters = self._build_scenario_adapters( reset_x0_before_opt=self.reset_x0_before_opt, keep_opt_history=True ) chain_dis, sub_opts = self._build_chain_dis_sub_opts() if self.__parallel_scenarios: use_threading = self._multithread_scenarios par_chain = MDOParallelChain( sub_opts, use_threading=use_threading, grammar_type=self._grammar_type ) chain_dis += [par_chain] else: # Chain MDA -> scenarios exec -> MDA chain_dis += sub_opts # Add MDA2 if needed if self._mda2: chain_dis += [self._mda2] if not self.reset_x0_before_opt: self.chain = MDOWarmStartedChain( chain_dis, name="bilevel_chain", grammar_type=self._grammar_type, variable_names_to_warm_start=self._get_variable_names_to_warm_start(), ) else: self.chain = MDOChain( chain_dis, name="bilevel_chain", grammar_type=self._grammar_type ) def _get_variable_names_to_warm_start(self) -> list[str]: """Retrieve the names of the variables to warm start. The outputs of all the sub scenarios that shall be warm started. Returns: The names of the variables to warm start. """ return [ name for adapter in self.scenario_adapters for name in adapter.get_output_data_names() ] def _update_design_space(self) -> None: """Update the design space by removing the coupling variables.""" self._set_default_input_values_from_design_space() self._remove_sub_scenario_dv_from_ds() self._remove_couplings_from_ds() self._remove_unused_variables() def _remove_couplings_from_ds(self) -> None: """Removes the coupling variables from the design space.""" if hasattr(self._mda2, "strong_couplings"): # Otherwise, the MDA2 may be a user provided MDA # Which manages the couplings internally couplings = self.mda2.strong_couplings design_space = self.opt_problem.design_space for coupling in couplings: if coupling in design_space.variable_names: design_space.remove_variable(coupling)
[docs] def get_top_level_disc(self) -> list[MDODiscipline]: # noqa:D102 return [self.chain]
[docs] def get_expected_workflow( # noqa:D102 self, ) -> list[ExecutionSequence, tuple[ExecutionSequence]]: return self.chain.get_expected_workflow()
[docs] def get_expected_dataflow( # noqa:D102 self, ) -> list[tuple[MDODiscipline, MDODiscipline, list[str]]]: return self.chain.get_expected_dataflow()
[docs] def add_constraint( self, output_name: str, constraint_type: MDOFunction.ConstraintType = MDOFunction.ConstraintType.EQ, constraint_name: str | None = None, value: float | None = None, positive: bool = False, levels: list[str] | None = None, ) -> None: """Add a constraint to the formulation. Args: levels: The levels at which the constraint is to be added (sublist of Bilevel.LEVELS). By default, the policy set at the initialization of the formulation is enforced. Raises: ValueError: When the constraint levels are not a sublist of BiLevel.LEVELS. """ # If the constraint levels are not specified the initial policy is enforced. if levels is None: if self._apply_cstr_to_system: self._add_system_level_constraint( output_name, constraint_type, constraint_name, value, positive ) if self._apply_cstr_tosub_scenarios: self._add_sub_level_constraint( output_name, constraint_type, constraint_name, value, positive ) # Otherwise the constraint is applied at the specified levels. elif not isinstance(levels, list) or not set(levels) <= set(BiLevel.LEVELS): msg = f"Constraint levels must be a sublist of {BiLevel.LEVELS}" raise ValueError(msg) elif not levels: LOGGER.warning("Empty list of constraint levels, constraint not added") else: if BiLevel.SYSTEM_LEVEL in levels: self._add_system_level_constraint( output_name, constraint_type, constraint_name, value, positive ) if BiLevel.SUBSCENARIOS_LEVEL in levels: self._add_sub_level_constraint( output_name, constraint_type, constraint_name, value, positive )
def _add_system_level_constraint( self, output_name: str, constraint_type: MDOFunction.ConstraintType = MDOFunction.ConstraintType.EQ, constraint_name: str | None = None, value: float | None = None, positive: bool = False, ) -> None: """Add a constraint at the system level. Args: output_name: The name of the output to be used as a constraint. For instance, if g_1 is given and constraint_type="eq", g_1=0 will be added as a constraint to the optimizer. constraint_type: The type of constraint, either "eq" for equality constraint or "ineq" for inequality constraint. constraint_name: The name of the constraint to be stored, If ``None``, the name is generated from the output name. value: The value of activation of the constraint. If ``None``, the value is equal to 0. positive: Whether the inequality constraint is positive. """ super().add_constraint( output_name, constraint_type, constraint_name, value, positive ) def _add_sub_level_constraint( self, output_name: str, constraint_type: MDOFunction.ConstraintType = MDOFunction.ConstraintType.EQ, constraint_name: str | None = None, value: float | None = None, positive: bool = False, ) -> None: """Add a constraint at the sub-scenarios level. Args: output_name: The name of the output to be used as a constraint. For instance, if g_1 is given and constraint_type="eq", g_1=0 will be added as a constraint to the optimizer. constraint_type: The type of constraint, either "eq" for equality constraint or "ineq" for inequality constraint. constraint_name: The name of the constraint to be stored, If ``None``, the name is generated from the output name. value: The value of activation of the constraint. If ``None``, the value is equal to 0. positive: Whether the inequality constraint is positive. Raises: ValueError: If a constraint is not found in the scenario top-level disciplines outputs. """ added = False output_names = self._check_add_cstr_input(output_name, constraint_type) for sub_scenario in self.get_sub_scenarios(): if self._scenario_computes_outputs(sub_scenario, output_names): sub_scenario.add_constraint( output_names, constraint_type, constraint_name, value, positive ) added = True if not added: msg = ( f"No sub scenario has an output named {output_name} " "cannot create such a constraint." ) raise ValueError(msg) @staticmethod def _scenario_computes_outputs( scenario: Scenario, output_names: Iterable[str], ) -> bool: """Check if the top level disciplines compute the given outputs. Args: output_names: The names of the variable to check. scenario: The scenario to be tested. Returns: True if the top level disciplines compute the given outputs. """ for disc in scenario.formulation.get_top_level_disc(): if disc.is_all_outputs_existing(output_names): return True return False