Source code for gemseo.algos.opt_problem

# Copyright 2022 Airbus SAS
# 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: Damien Guenot
#       :author: Francois Gallard, Charlie Vanaret, Benoit Pauwels
#       :author: Gabriel Max De Mendonça Abrantes
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
r"""Optimization problem.

The :class:`.OptimizationProblem` class operates on a :class:`.DesignSpace` defining:

- an initial guess :math:`x_0` for the design variables,
- the bounds :math:`l_b \leq x \leq u_b` of the design variables.

A (possible vector) objective function with an :class:`.MDOFunction` type
is set using the ``objective`` attribute.
If the optimization problem looks for the maximum of this objective function,
the :meth:`.OptimizationProblem.change_objective_sign`
changes the objective function sign
because the optimization drivers seek to minimize this objective function.

Equality and inequality constraints are also :class:`.MDOFunction` instances
provided to the :class:`.OptimizationProblem`
by means of its :meth:`.OptimizationProblem.add_constraint` method.

The :class:`.OptimizationProblem` allows to evaluate the different functions
for a given design parameters vector
(see :meth:`.OptimizationProblem.evaluate_functions`).
Note that this evaluation step relies on an automated scaling of function wrt the bounds
so that optimizers and DOE algorithms work
with inputs scaled between 0 and 1 for all the variables.

The :class:`.OptimizationProblem`  has also a :class:`.Database`
that stores the calls to all the functions
so that no function is called twice with the same inputs.
Concerning the derivatives' computation,
the :class:`.OptimizationProblem` automates
the generation of the finite differences or complex step wrappers on functions,
when the analytical gradient is not available.

Lastly,
various getters and setters are available,
as well as methods to export the :class:`.Database`
to an HDF file or to a :class:`.Dataset` for future post-processing.
"""

from __future__ import annotations

import contextlib
import logging
from collections.abc import Iterable
from collections.abc import Mapping
from collections.abc import Sequence
from copy import deepcopy
from functools import reduce
from numbers import Number
from typing import TYPE_CHECKING
from typing import Any
from typing import Callable
from typing import ClassVar
from typing import Final
from typing import Optional
from typing import Union

import h5py
import numpy
from h5py import Group
from numpy import abs as np_abs
from numpy import all as np_all
from numpy import any as np_any
from numpy import argmin
from numpy import array
from numpy import array_equal
from numpy import atleast_1d
from numpy import bytes_
from numpy import eye as np_eye
from numpy import hstack
from numpy import inf
from numpy import insert
from numpy import isnan
from numpy import issubdtype
from numpy import multiply
from numpy import nan
from numpy import ndarray
from numpy import number as np_number
from numpy import where
from numpy import zeros
from numpy.linalg import norm
from pandas import MultiIndex
from strenum import StrEnum

from gemseo.algos.aggregation.aggregation_func import aggregate_iks
from gemseo.algos.aggregation.aggregation_func import aggregate_lower_bound_ks
from gemseo.algos.aggregation.aggregation_func import aggregate_max
from gemseo.algos.aggregation.aggregation_func import aggregate_positive_sum_square
from gemseo.algos.aggregation.aggregation_func import aggregate_sum_square
from gemseo.algos.aggregation.aggregation_func import aggregate_upper_bound_ks
from gemseo.algos.base_problem import BaseProblem
from gemseo.algos.database import Database
from gemseo.algos.design_space import DesignSpace
from gemseo.algos.opt_result import OptimizationResult
from gemseo.algos.opt_result_multiobj import MultiObjectiveOptimizationResult
from gemseo.algos.pareto import ParetoFront
from gemseo.core.mdofunctions.dense_jacobian_function import DenseJacobianFunction
from gemseo.core.mdofunctions.func_operations import LinearComposition
from gemseo.core.mdofunctions.mdo_function import MDOFunction
from gemseo.core.mdofunctions.mdo_linear_function import MDOLinearFunction
from gemseo.core.mdofunctions.mdo_quadratic_function import MDOQuadraticFunction
from gemseo.core.mdofunctions.norm_db_function import NormDBFunction
from gemseo.core.mdofunctions.norm_function import NormFunction
from gemseo.datasets.dataset import Dataset
from gemseo.datasets.io_dataset import IODataset
from gemseo.datasets.optimization_dataset import OptimizationDataset
from gemseo.disciplines.constraint_aggregation import ConstraintAggregation
from gemseo.utils.compatibility.scipy import sparse_classes
from gemseo.utils.constants import READ_ONLY_EMPTY_DICT
from gemseo.utils.derivatives.approximation_modes import ApproximationMode
from gemseo.utils.derivatives.gradient_approximator_factory import (
    GradientApproximatorFactory,
)
from gemseo.utils.enumeration import merge_enums
from gemseo.utils.hdf5 import get_hdf5_group
from gemseo.utils.string_tools import MultiLineString
from gemseo.utils.string_tools import pretty_str

if TYPE_CHECKING:
    from pathlib import Path

    from numpy.typing import NDArray

LOGGER = logging.getLogger(__name__)

BestInfeasiblePointType = tuple[
    Optional[ndarray], Optional[ndarray], bool, dict[str, ndarray]
]
OptimumType = tuple[ndarray, ndarray, bool, dict[str, ndarray], dict[str, ndarray]]
OptimumSolutionType = tuple[
    Optional[Sequence[ndarray]], ndarray, dict[str, ndarray], dict[str, ndarray]
]
EvaluationType = tuple[dict[str, Union[float, ndarray]], dict[str, ndarray]]
"""The type of the output value of an evaluation."""


[docs] class OptimizationProblem(BaseProblem): """An optimization problem. Create an optimization problem from: - a :class:`.DesignSpace` specifying the design variables in terms of names, lower bounds, upper bounds and initial guesses, - the objective function as an :class:`.MDOFunction`, which can be a vector, execute it from an algorithm provided by a :class:`.DriverLibrary`, and store some execution data in a :class:`.Database`. In particular, this :class:`.Database` stores the calls to all the functions so that no function is called twice with the same inputs. An :class:`.OptimizationProblem` also has an automated scaling of function with respect to the bounds of the design variables so that the driving algorithms work with inputs scaled between 0 and 1. Lastly, :class:`.OptimizationProblem` automates the generation of finite differences or complex step wrappers on functions, when analytical gradient is not available. """ current_iter: int """The current iteration.""" max_iter: int """The maximum iteration.""" nonproc_objective: MDOFunction """The non-processed objective function.""" constraints: list[MDOFunction] """The constraints.""" nonproc_constraints: list[MDOFunction] """The non-processed constraints.""" observables: list[MDOFunction] """The observables.""" new_iter_observables: list[MDOFunction] """The observables to be called at each new iterate.""" nonproc_observables: list[MDOFunction] """The non-processed observables.""" nonproc_new_iter_observables: list[MDOFunction] """The non-processed observables to be called at each new iterate.""" __minimize_objective: bool """Whether to minimize the objective.""" fd_step: float """The finite differences step.""" pb_type: ProblemType """The type of optimization problem.""" ineq_tolerance: float """The tolerance for the inequality constraints.""" eq_tolerance: float """The tolerance for the equality constraints.""" database: Database """The database to store the optimization problem data.""" solution: OptimizationResult | None """The solution of the optimization problem if solved; otherwise ``None``.""" design_space: DesignSpace """The design space on which the optimization problem is solved.""" stop_if_nan: bool """Whether the optimization stops when a function returns ``NaN``.""" preprocess_options: dict """The options to pre-process the functions.""" use_standardized_objective: bool """Whether to use standardized objective for logging and post-processing. The standardized objective corresponds to the original one expressed as a cost function to minimize. A :class:`.DriverLibrary` works with this standardized objective and the :class:`.Database` stores its values. However, for convenience, it may be more relevant to log the expression and the values of the original objective. """ AggregationFunction = ConstraintAggregation.EvaluationFunction _AGGREGATION_FUNCTION_MAP: Final[str] = { AggregationFunction.IKS: aggregate_iks, AggregationFunction.LOWER_BOUND_KS: aggregate_lower_bound_ks, AggregationFunction.UPPER_BOUND_KS: aggregate_upper_bound_ks, AggregationFunction.POS_SUM: aggregate_positive_sum_square, AggregationFunction.MAX: aggregate_max, AggregationFunction.SUM: aggregate_sum_square, }
[docs] class ProblemType(StrEnum): """The type of problem.""" LINEAR = "linear" NON_LINEAR = "non-linear"
ApproximationMode = ApproximationMode class _DifferentiationMethod(StrEnum): """The additional differentiation methods.""" USER_GRAD = "user" NO_DERIVATIVE = "no_derivative" DifferentiationMethod = merge_enums( "DifferentiationMethod", StrEnum, ApproximationMode, _DifferentiationMethod, doc="The differentiation methods.", ) DESIGN_VAR_NAMES: Final[str] = "x_names" DESIGN_VAR_SIZE: Final[str] = "x_size" DESIGN_SPACE_ATTRS: Final[str] = [ "u_bounds", "l_bounds", "x_0", DESIGN_VAR_NAMES, "dimension", ] FUNCTIONS_ATTRS: ClassVar[str] = ["objective", "constraints"] OPTIM_DESCRIPTION: ClassVar[str] = [ "minimize_objective", "fd_step", "differentiation_method", "pb_type", "ineq_tolerance", "eq_tolerance", ] OPT_DESCR_GROUP: Final[str] = "opt_description" DESIGN_SPACE_GROUP: Final[str] = "design_space" OBJECTIVE_GROUP: Final[str] = "objective" SOLUTION_GROUP: Final[str] = "solution" CONSTRAINTS_GROUP: Final[str] = "constraints" OBSERVABLES_GROUP: Final[str] = "observables" activate_bound_check: ClassVar[bool] = True """Whether to check if a point is in the design space before calling functions.""" HDF5_FORMAT: Final[str] = "hdf5" HDF_NODE_PATH: Final[str] = "hdf_node_path" GGOBI_FORMAT: Final[str] = "ggobi" KKT_RESIDUAL_NORM: Final[str] = "KKT residual norm" def __init__( self, design_space: DesignSpace, pb_type: ProblemType = ProblemType.LINEAR, input_database: str | Path | Database | None = None, differentiation_method: DifferentiationMethod = DifferentiationMethod.USER_GRAD, fd_step: float = 1e-7, parallel_differentiation: bool = False, use_standardized_objective: bool = True, hdf_node_path: str = "", **parallel_differentiation_options: int | bool, ) -> None: """ Args: design_space: The design space on which the functions are evaluated. pb_type: The type of the optimization problem. input_database: A database to initialize that of the optimization problem. If ``None``, the optimization problem starts from an empty database. differentiation_method: The default differentiation method to be applied to the functions of the optimization problem. fd_step: The step to be used by the step-based differentiation methods. parallel_differentiation: Whether to approximate the derivatives in parallel. use_standardized_objective: Whether to use standardized objective for logging and post-processing. hdf_node_path: The path of the HDF node from which the database should be imported. If empty, the root node is considered. **parallel_differentiation_options: The options to approximate the derivatives in parallel. """ # noqa: D205, D212, D415 self._objective = None self.nonproc_objective = None self.constraints = [] self.nonproc_constraints = [] self.observables = [] self.new_iter_observables = [] self.nonproc_observables = [] self.nonproc_new_iter_observables = [] self.__minimize_objective = True self.fd_step = fd_step self.__differentiation_method = None self.differentiation_method = differentiation_method self.pb_type = pb_type self.ineq_tolerance = 1e-4 self.eq_tolerance = 1e-2 self.max_iter = 0 self.current_iter = 0 self.use_standardized_objective = use_standardized_objective self.__functions_are_preprocessed = False if input_database is None: self.database = Database() elif isinstance(input_database, Database): self.database = input_database else: self.database = Database.from_hdf( input_database, hdf_node_path=hdf_node_path ) self.solution = None self.design_space = design_space self.__initial_current_x = deepcopy( design_space.get_current_value(as_dict=True) ) self.__x0 = None self.stop_if_nan = True self.preprocess_options = {} self.__parallel_differentiation = parallel_differentiation self.__parallel_differentiation_options = parallel_differentiation_options self.__eval_obs_jac = False self.__observable_names = set() def __raise_exception_if_functions_are_already_preprocessed(self) -> None: """Raise an exception if the function have already been pre-processed.""" if self.__functions_are_preprocessed: msg = ( "The parallel differentiation cannot be changed " "because the functions have already been pre-processed." ) raise RuntimeError(msg)
[docs] def is_max_iter_reached(self) -> bool: """Check if the maximum amount of iterations has been reached. Returns: Whether the maximum amount of iterations has been reached. """ if self.max_iter in {None, 0} or self.current_iter in {None, 0}: return False return self.current_iter >= self.max_iter
@property def constraint_names(self) -> dict[str, list[str]]: """The standardized constraint names bound to the original ones.""" names = {} for constraint in self.constraints: if constraint.original_name not in names: names[constraint.original_name] = [] names[constraint.original_name].append(constraint.name) return names @property def parallel_differentiation(self) -> bool: """Whether to approximate the derivatives in parallel.""" return self.__parallel_differentiation @parallel_differentiation.setter def parallel_differentiation( self, value: bool, ) -> None: self.__raise_exception_if_functions_are_already_preprocessed() self.__parallel_differentiation = value @property def parallel_differentiation_options(self) -> dict[str, int | bool]: """The options to approximate the derivatives in parallel.""" return self.__parallel_differentiation_options @parallel_differentiation_options.setter def parallel_differentiation_options(self, value: dict[str, int | bool]) -> None: self.__raise_exception_if_functions_are_already_preprocessed() self.__parallel_differentiation_options = value @property def objective(self) -> MDOFunction: """The objective function.""" return self._objective @objective.setter def objective( self, func: MDOFunction, ) -> None: func.f_type = func.FunctionType.OBJ if self.pb_type == self.ProblemType.LINEAR and not isinstance( func, MDOLinearFunction ): self.pb_type = self.ProblemType.NON_LINEAR self._objective = func @property def minimize_objective(self) -> bool: """Whether to minimize the objective.""" return self.__minimize_objective @minimize_objective.setter def minimize_objective(self, value: bool) -> None: if self.__minimize_objective != value: self.change_objective_sign()
[docs] @staticmethod def repr_constraint( func: MDOFunction, cstr_type: MDOFunction.ConstraintType, value: float | None = None, positive: bool = False, ) -> str: """Express a constraint as a string expression. Args: func: The constraint function. cstr_type: The type of the constraint. value: The value for which the constraint is active. If ``None``, this value is 0. positive: If ``True``, then the inequality constraint is positive. Returns: A string representation of the constraint. """ if value is None: value = 0.0 str_repr = func.name if func.input_names: arguments = ", ".join(func.input_names) str_repr += f"({arguments})" if cstr_type == MDOFunction.ConstraintType.EQ: sign = " == " elif positive: sign = " >= " else: sign = " <= " if func.expr: str_repr += ": " expr = func.expr n_char = len(str_repr) # Remove empty lines with filter expr_spl = [_f for _f in expr.split("\n") if _f] str_repr = str_repr + expr_spl[0] + sign + str(value) if isinstance(func, (MDOLinearFunction, MDOQuadraticFunction)): for repre in expr_spl[1:]: str_repr += "\n" + " " * n_char + repre else: for repre in expr_spl[1:]: str_repr += "\n" + " " * n_char + repre + sign + str(value) else: str_repr += sign + str(value) return str_repr
[docs] def add_constraint( self, cstr_func: MDOFunction, value: float | None = None, cstr_type: MDOFunction.ConstraintType | None = None, positive: bool = False, ) -> None: """Add a constraint (equality and inequality) to the optimization problem. Args: cstr_func: The constraint. value: The value for which the constraint is active. If ``None``, this value is 0. cstr_type: The type of the constraint. positive: If ``True``, then the inequality constraint is positive. Raises: TypeError: When the constraint of a linear optimization problem is not an :class:`.MDOLinearFunction`. ValueError: When the type of the constraint is missing. """ func_name = cstr_func.name has_default_name = cstr_func.has_default_name self.check_format(cstr_func) if self.pb_type == OptimizationProblem.ProblemType.LINEAR and not isinstance( cstr_func, MDOLinearFunction ): self.pb_type = OptimizationProblem.ProblemType.NON_LINEAR ctype = cstr_type or cstr_func.f_type cstr_repr = self.repr_constraint(cstr_func, ctype, value, positive) if value is not None: cstr_func = cstr_func.offset(-value) if positive: cstr_func = -cstr_func if cstr_type is not None: cstr_func.f_type = cstr_type elif not cstr_func.is_constraint(): msg = ( "Constraint type must be provided, " "either in the function attributes or to the add_constraint method." ) raise ValueError(msg) cstr_func.special_repr = cstr_repr self.constraints.append(cstr_func) if not has_default_name: cstr_func.name = func_name if cstr_func.output_names: output_names = "#".join(cstr_func.output_names) cstr_repr = cstr_repr.replace(func_name, output_names) cstr_func.expr = cstr_func.expr.replace(func_name, output_names) cstr_func.special_repr = f"{func_name}: {cstr_repr}"
[docs] def add_eq_constraint( self, cstr_func: MDOFunction, value: float | None = None, ) -> None: """Add an equality constraint to the optimization problem. Args: cstr_func: The constraint. value: The value for which the constraint is active. If ``None``, this value is 0. """ self.add_constraint(cstr_func, value, cstr_type=MDOFunction.ConstraintType.EQ)
[docs] def add_ineq_constraint( self, cstr_func: MDOFunction, value: float | None = None, positive: bool = False, ) -> None: """Add an inequality constraint to the optimization problem. Args: cstr_func: The constraint. value: The value for which the constraint is active. If ``None``, this value is 0. positive: If ``True``, then the inequality constraint is positive. """ self.add_constraint( cstr_func, value, cstr_type=MDOFunction.ConstraintType.INEQ, positive=positive, )
[docs] def aggregate_constraint( self, constraint_index: int, method: Callable[[NDArray[float]], float] | AggregationFunction = AggregationFunction.MAX, groups: Iterable[Sequence[int]] | None = None, **options: Any, ) -> None: """Aggregate a constraint to generate a reduced dimension constraint. Args: constraint_index: The index of the constraint in :attr:`.constraints`. method: The aggregation method, e.g. ``"max"``, ``"lower_bound_KS"``, ``"upper_bound_KS"``or ``"IKS"``. groups: The groups of components of the constraint to aggregate to produce one aggregation constraint per group of components; if ``None``, a single aggregation constraint is produced. **options: The options of the aggregation method. Raises: ValueError: When the given index is greater or equal than the number of constraints or when the constraint aggregation method is unknown. """ n_constraints = len(self.constraints) self.pb_type = OptimizationProblem.ProblemType.NON_LINEAR if constraint_index >= n_constraints: msg = ( f"The index of the constraint ({constraint_index}) must be lower " f"than the number of constraints ({n_constraints})." ) raise KeyError(msg) constraint = self.constraints[constraint_index] if callable(method): aggregate_constraints = method else: aggregate_constraints = self._AGGREGATION_FUNCTION_MAP[method] del self.constraints[constraint_index] if groups is None: self.constraints.insert( constraint_index, aggregate_constraints(constraint, **options) ) else: self.constraints[constraint_index:constraint_index] = [ aggregate_constraints(constraint, indices, **options) for indices in groups ]
[docs] def apply_exterior_penalty( self, objective_scale: float = 1.0, scale_inequality: float | ndarray = 1.0, scale_equality: float | ndarray = 1.0, ) -> None: r"""Reformulate the optimization problem using exterior penalty. Given the optimization problem with equality and inequality constraints: .. math:: min_x f(x) s.t. g(x)\leq 0 h(x)=0 l_b\leq x\leq u_b The exterior penalty approach consists in building a penalized objective function that takes into account constraints violations: .. math:: min_x \tilde{f}(x) = \frac{f(x)}{o_s} + s[\sum{H(g(x))g(x)^2}+\sum{h(x)^2}] s.t. l_b\leq x\leq u_b Where :math:`H(x)` is the Heaviside function, :math:`o_s` is the ``objective_scale`` parameter and :math:`s` is the scale parameter. The solution of the new problem approximate the one of the original problem. Increasing the values of ``objective_scale`` and scale, the solutions are closer but the optimization problem requires more and more iterations to be solved. Args: scale_equality: The equality constraint scaling constant. objective_scale: The objective scaling constant. scale_inequality: The inequality constraint scaling constant. """ self.pb_type = OptimizationProblem.ProblemType.NON_LINEAR penalized_objective = self.objective / objective_scale self.add_observable(self.objective) for constr in self.constraints: if constr.f_type == MDOFunction.ConstraintType.INEQ: penalized_objective += aggregate_positive_sum_square( constr, scale=scale_inequality ) else: penalized_objective += aggregate_sum_square( constr, scale=scale_equality ) self.add_observable(constr) self.objective = penalized_objective self.constraints = []
[docs] def get_reformulated_problem_with_slack_variables(self) -> OptimizationProblem: r"""Add slack variables and replace inequality constraints with equality ones. Given the original optimization problem, .. math:: min_x f(x) s.t. g(x)\leq 0 h(x)=0 l_b\leq x\leq u_b Slack variables are introduced for all inequality constraints that are non-positive. An equality constraint for each slack variable is then defined. .. math:: min_{x,s} F(x,s) = f(x) s.t. H(x,s) = h(x)=0 G(x,s) = g(x)-s=0 l_b\leq x\leq u_b s\leq 0 Returns: An optimization problem without inequality constraints. """ # Copy the original design space problem = OptimizationProblem(deepcopy(self.design_space)) # Evaluate the MDOFunctions. self.evaluate_functions() # Add a slack variable to the copied design space for each # inequality constraint. for constr in self.get_ineq_constraints(): problem.design_space.add_variable( name=f"slack_variable_{constr.name}", size=constr.dim, value=0, u_b=0, ) # Compute a restriction operator that goes from the new design space to the old # design space variables. restriction_operator = hstack(( np_eye(self.dimension), zeros((self.dimension, problem.dimension - self.dimension)), )) # Get the new problem objective function composing the initial objective # function with the restriction operator. problem.objective = LinearComposition(self.objective, restriction_operator) # Each constraint is passed to the new problem. Each inequality constraints is # modified first composing the initial constraint with the restriction operator # then subtracting s. Where s is the constraint slack variable previously built. # Each equality constraint is added composing the initial constraint with the # restriction operator. for constr in self.constraints: new_function = LinearComposition(constr, restriction_operator) if constr.f_type == MDOFunction.ConstraintType.EQ: problem.add_eq_constraint(new_function) continue coefficients = where( [ i in problem.design_space.get_variables_indexes( f"slack_variable_{constr.name}" ) for i in range(problem.dimension) ], -1, 0, ) correction_term = MDOLinearFunction( coefficients=coefficients, name=f"offset_{constr.name}", input_names=problem.design_space.get_indexed_variable_names(), ) problem.add_eq_constraint(new_function + correction_term) return problem
[docs] def add_observable( self, obs_func: MDOFunction, new_iter: bool = True, ) -> None: """Add a function to be observed. When the :class:`.OptimizationProblem` is executed, the observables are called following this sequence: - The optimization algorithm calls the objective function with a normalized ``x_vect``. - The :meth:`.OptimizationProblem.preprocess_functions` wraps the function as a :class:`.NormDBFunction`, which unnormalizes the ``x_vect`` before evaluation. - The unnormalized ``x_vect`` and the result of the evaluation are stored in the :attr:`.OptimizationProblem.database`. - The previous step triggers the :attr:`.OptimizationProblem.new_iter_listeners`, which calls the observables with the unnormalized ``x_vect``. - The observables themselves are wrapped as a :class:`.NormDBFunction` by :meth:`.OptimizationProblem.preprocess_functions`, but in this case the input is always expected as unnormalized to avoid an additional normalizing-unnormalizing step. - Finally, the output is stored in the :attr:`.OptimizationProblem.database`. Args: obs_func: An observable to be observed. new_iter: If ``True``, then the observable will be called at each new iterate. """ name = obs_func.name if name in self.__observable_names: LOGGER.warning('The optimization problem already observes "%s".', name) return self.check_format(obs_func) obs_func.f_type = MDOFunction.FunctionType.OBS self.observables.append(obs_func) self.__observable_names.add(name) if new_iter: self.new_iter_observables.append(obs_func)
[docs] def get_eq_constraints(self) -> list[MDOFunction]: """Retrieve all the equality constraints. Returns: The equality constraints. """ def is_equality_constraint( func: MDOFunction, ) -> bool: """Check if a function is an equality constraint. Args: func: A function. Returns: True if the function is an equality constraint. """ return func.f_type == MDOFunction.ConstraintType.EQ return list(filter(is_equality_constraint, self.constraints))
[docs] def get_ineq_constraints(self) -> list[MDOFunction]: """Retrieve all the inequality constraints. Returns: The inequality constraints. """ def is_inequality_constraint( func: MDOFunction, ) -> bool: """Check if a function is an inequality constraint. Args: func: A function. Returns: True if the function is an inequality constraint. """ return func.f_type == MDOFunction.ConstraintType.INEQ return list(filter(is_inequality_constraint, self.constraints))
[docs] def get_observable(self, name: str) -> MDOFunction: """Return an observable of the problem. Args: name: The name of the observable. Returns: The pre-processed observable if the functions of the problem have already been pre-processed, otherwise the original one. """ return self.__get_observable(name, not self.__functions_are_preprocessed)
def __get_observable( self, name: str, from_original_observables: bool ) -> MDOFunction: """Return an observable of the problem. Args: name: The name of the observable. from_original_observables: Whether to get the observable from the original observables; otherwise return the observable from the pre-processed observables. Returns: The observable. """ return self.__get_function( name, from_original_observables, self.__observable_names, self.nonproc_observables, self.observables, self.OBSERVABLES_GROUP, )
[docs] def get_ineq_constraints_number(self) -> int: """Retrieve the number of inequality constraints. Returns: The number of inequality constraints. """ return len(self.get_ineq_constraints())
[docs] def get_eq_constraints_number(self) -> int: """Retrieve the number of equality constraints. Returns: The number of equality constraints. """ return len(self.get_eq_constraints())
[docs] def get_constraints_number(self) -> int: """Retrieve the number of constraints. Returns: The number of constraints. """ return len(self.constraints)
[docs] def get_constraint_names(self) -> list[str]: """Retrieve the names of the constraints. Returns: The names of the constraints. """ return [constraint.name for constraint in self.constraints]
[docs] def get_nonproc_constraints(self) -> list[MDOFunction]: """Retrieve the non-processed constraints. Returns: The non-processed constraints. """ return self.nonproc_constraints
[docs] def get_design_variable_names(self) -> list[str]: """Retrieve the names of the design variables. Returns: The names of the design variables. """ return self.design_space.variable_names
[docs] def get_all_functions(self, original: bool = False) -> list[MDOFunction]: """Retrieve all the functions of the optimization problem. These functions are the constraints, the objective function and the observables. Args: original: Whether to return the original functions or the preprocessed ones. Returns: All the functions of the optimization problem. """ if self.__functions_are_preprocessed and original: return [ self.nonproc_objective, *self.nonproc_constraints, *self.nonproc_observables, ] return [self.objective, *self.constraints, *self.observables]
[docs] def get_all_function_name(self) -> list[str]: """Retrieve the names of all the function of the optimization problem. These functions are the constraints, the objective function and the observables. Returns: The names of all the functions of the optimization problem. """ return [func.name for func in self.get_all_functions()]
[docs] def get_objective_name(self, standardize: bool = True) -> str: """Retrieve the name of the objective function. Args: standardize: Whether to use the name of the objective expressed as a cost, e.g. ``"-f"`` when the user seeks to maximize ``"f"``. Returns: The name of the objective function. """ if standardize or self.minimize_objective: return self.objective.name return self.objective.name[1:]
[docs] def get_function_names(self, names: Iterable[str]) -> list[str]: """Return the names of the functions stored in the database. Args: names: The names of the outputs or constraints specified by the user. Returns: The names of the constraints stored in the database. """ user_constraint_names = self.constraint_names.keys() function_names = [] for name in names: if name in user_constraint_names: function_names.extend(self.constraint_names[name]) else: function_names.append(name) return function_names
[docs] def get_nonproc_objective(self) -> MDOFunction: """Retrieve the non-processed objective function.""" return self.nonproc_objective
[docs] def has_nonlinear_constraints(self) -> bool: """Check if the problem has non-linear constraints. Returns: True if the problem has equality or inequality constraints. """ return len(self.constraints) > 0
[docs] def has_constraints(self) -> bool: """Check if the problem has equality or inequality constraints. Returns: True if the problem has equality or inequality constraints. """ return self.has_eq_constraints() or self.has_ineq_constraints()
[docs] def has_eq_constraints(self) -> bool: """Check if the problem has equality constraints. Returns: True if the problem has equality constraints. """ return len(self.get_eq_constraints()) > 0
[docs] def has_ineq_constraints(self) -> bool: """Check if the problem has inequality constraints. Returns: True if the problem has inequality constraints. """ return len(self.get_ineq_constraints()) > 0
[docs] def get_x0_normalized( self, cast_to_real: bool = False, as_dict: bool = False ) -> ndarray | dict[str, ndarray]: """Return the initial values of the design variables after normalization. Args: cast_to_real: Whether to return the real part of the initial values. as_dict: Whether to return the values as a dictionary of the form ``{variable_name: variable_value}``. Returns: The current values of the design variables normalized between 0 and 1 from their lower and upper bounds. """ return self.design_space.get_current_value(None, cast_to_real, as_dict, True)
[docs] def get_dimension(self) -> int: """Retrieve the total number of design variables. Returns: The dimension of the design space. """ return self.design_space.dimension
@property def dimension(self) -> int: """The dimension of the design space.""" return self.design_space.dimension
[docs] @staticmethod def check_format(input_function: Any) -> None: """Check that a function is an instance of :class:`.MDOFunction`. Args: input_function: The function to be tested. Raises: TypeError: If the function is not an :class:`.MDOFunction`. """ if not isinstance(input_function, MDOFunction): msg = "Optimization problem functions must be instances of MDOFunction" raise TypeError(msg)
[docs] def get_eq_cstr_total_dim(self) -> int: """Retrieve the total dimension of the equality constraints. This dimension is the sum of all the outputs dimensions of all the equality constraints. Returns: The total dimension of the equality constraints. """ return self.__count_cstr_total_dim(MDOFunction.ConstraintType.EQ)
[docs] def get_ineq_cstr_total_dim(self) -> int: """Retrieve the total dimension of the inequality constraints. This dimension is the sum of all the outputs dimensions of all the inequality constraints. Returns: The total dimension of the inequality constraints. """ return self.__count_cstr_total_dim(MDOFunction.ConstraintType.INEQ)
def __count_cstr_total_dim( self, cstr_type: str, ) -> int: """Retrieve the total dimension of the constraints. This dimension is the sum of all the outputs dimensions of all the constraints. of equality or inequality constraints dimensions that is the sum of all outputs dimensions of all constraints. Returns: The total dimension of the constraints. """ n_cstr = 0 for constraint in self.constraints: if not constraint.dim: msg = ( "Constraint dimension not available yet, " f"please call function {constraint} once" ) raise ValueError(msg) if constraint.f_type == cstr_type: n_cstr += constraint.dim return n_cstr
[docs] def get_active_ineq_constraints( self, x_vect: ndarray, tol: float = 1e-6, ) -> dict[MDOFunction, ndarray]: """For each constraint, indicate if its different components are active. Args: x_vect: The vector of design variables. tol: The tolerance for deciding whether a constraint is active. Returns: For each constraint, a boolean indicator of activation of its different components. """ self.design_space.check_membership(x_vect) if self.preprocess_options.get("is_function_input_normalized", False): x_vect = self.design_space.normalize_vect(x_vect) return { func: atleast_1d((func(x_vect)) >= -tol) for func in self.get_ineq_constraints() }
# TODO: API: rename callback_func to callback
[docs] def add_callback( self, callback_func: Callable[[ndarray], Any], each_new_iter: bool = True, each_store: bool = False, ) -> None: """Add a callback for some events. The callback functions are attached to the database, which means they are triggered when new values are stored within the database of the optimization problem. Args: callback_func: A function to be called after some events, whose argument is a design vector. each_new_iter: Whether to evaluate the callback functions after evaluating all functions of the optimization problem for a given point and storing their values in the :attr:`.database`. each_store: Whether to evaluate the callback functions after storing any new value in the :attr:`.database`. """ if each_store: self.database.add_store_listener(callback_func) if each_new_iter: self.database.add_new_iter_listener(callback_func)
[docs] def clear_listeners(self) -> None: """Clear all the listeners.""" self.database.clear_listeners()
[docs] def evaluate_functions( self, x_vect: ndarray = None, eval_jac: bool = False, eval_obj: bool = True, eval_observables: bool = True, normalize: bool = True, no_db_no_norm: bool = False, constraint_names: Iterable[str] | None = None, observable_names: Iterable[str] | None = None, jacobian_names: Iterable[str] | None = None, ) -> EvaluationType: """Compute the functions of interest, and possibly their derivatives. These functions of interest are the constraints, and possibly the objective. Some optimization libraries require the number of constraints as an input parameter which is unknown by the formulation or the scenario. Evaluation of initial point allows to get this mandatory information. This is also used for design of experiments to evaluate samples. Args: x_vect: The input vector at which the functions must be evaluated; if None, the initial point x_0 is used. eval_jac: Whether to compute the Jacobian matrices of the functions of interest. If ``True`` and ``jacobian_names`` is ``None`` then compute the Jacobian matrices (or gradients) of the functions that are selected for evaluation (with ``eval_obj``, ``constraint_names``, ``eval_observables`` and``observable_names``). If ``False`` and ``jacobian_names`` is ``None`` then no Jacobian matrix is evaluated. If ``jacobian_names`` is not ``None`` then the value of ``eval_jac`` is ignored. eval_obj: Whether to consider the objective function as a function of interest. eval_observables: Whether to evaluate the observables. If ``True`` and ``observable_names`` is ``None`` then all the observables are evaluated. If ``False`` and ``observable_names`` is ``None`` then no observable is evaluated. If ``observable_names`` is not ``None`` then the value of ``eval_observables`` is ignored. normalize: Whether to consider the input vector ``x_vect`` normalized. no_db_no_norm: If ``True``, then do not use the pre-processed functions, so we have no database, nor normalization. constraint_names: The names of the constraints to evaluate. If ``None`` then all the constraints are evaluated. observable_names: The names of the observables to evaluate. If ``None`` and ``eval_observables`` is ``True`` then all the observables are evaluated. If ``None`` and ``eval_observables`` is ``False`` then no observable is evaluated. jacobian_names: The names of the functions whose Jacobian matrices (or gradients) to compute. If ``None`` and ``eval_jac`` is ``True`` then compute the Jacobian matrices (or gradients) of the functions that are selected for evaluation (with ``eval_obj``, ``constraint_names``, ``eval_observables`` and``observable_names``). If ``None`` and ``eval_jac`` is ``False`` then no Jacobian matrix is computed. Returns: The output values of the functions of interest, as well as their Jacobian matrices if ``eval_jac`` is ``True``. Raises: ValueError: If a name in ``jacobian_names`` is not the name of a function of the problem. """ # Get the functions to be evaluated from_original_functions = not self.__functions_are_preprocessed or no_db_no_norm functions = self.__get_functions( eval_obj, constraint_names, observable_names, eval_observables, from_original_functions, ) # Evaluate the functions outputs = {} if functions: # N.B. either all functions expect normalized inputs or none of them do. preprocessed_x_vect = self.__preprocess_inputs( x_vect, normalize, functions[0].expects_normalized_inputs ) for function in functions: try: # Calling function.evaluate is faster than function() outputs[function.name] = function.evaluate(preprocessed_x_vect) except ValueError: # noqa: PERF203 LOGGER.exception("Failed to evaluate function %s", function.name) raise if not eval_jac and jacobian_names is None: return outputs, {} # Evaluate the Jacobians if jacobian_names is not None: unknown_names = set(jacobian_names) - set(self.get_all_function_name()) if unknown_names: if len(unknown_names) > 1: message = "These names are" else: message = "This name is" msg = ( f"{message} not among the names of the functions: " f"{pretty_str(unknown_names)}." ) raise ValueError(msg) functions = self.__get_functions( self.objective.name in jacobian_names, [name for name in jacobian_names if name in self.constraint_names], [name for name in jacobian_names if name in self.__observable_names], True, from_original_functions, ) if functions: # N.B. either all functions expect normalized inputs or none of them do. preprocessed_x_vect = self.__preprocess_inputs( x_vect, normalize, functions[0].expects_normalized_inputs ) jacobians = {} for function in functions: try: jacobians[function.name] = function.jac(preprocessed_x_vect) except ValueError: # noqa: PERF203 LOGGER.exception("Failed to evaluate Jacobian of %s.", function.name) raise return outputs, jacobians
def __preprocess_inputs( self, input_value: ndarray | None, normalized: bool, normalization_expected: bool, ) -> ndarray: """Prepare the design variables for the evaluation of functions. Args: input_value: The design variables. normalized: Whether the design variables are normalized. normalization_expected: Whether the functions expect normalized variables. Returns: The prepared variables. """ if input_value is None: input_value = self.design_space.get_current_value(normalize=normalized) elif self.activate_bound_check: if normalized: non_normalized_variables = self.design_space.unnormalize_vect( input_value, no_check=True ) else: non_normalized_variables = input_value self.design_space.check_membership(non_normalized_variables) if normalized and not normalization_expected: return self.design_space.unnormalize_vect(input_value, no_check=True) if not normalized and normalization_expected: return self.design_space.normalize_vect(input_value) return input_value def __get_functions( self, eval_obj: bool, constraint_names: Iterable[str] | None, observable_names: Iterable[str] | None, eval_observables: bool, from_original_functions: bool, ) -> list[MDOFunction]: """Return functions. Args: eval_obj: Whether to return the objective function. constraint_names: The names of the constraints to return. If ``None`` then all the constraints are evaluated. observable_names: The names of the observables to return. If ``None`` and ``eval_observables`` is True then all the observables are returned. If ``None`` and ``eval_observables`` is False then no observable is returned. eval_observables: Whether to return the observables. from_original_functions: Whether to get the functions from the original ones; otherwise get the functions from the pre-processed ones. Returns: The functions to be evaluated or differentiated. """ use_nonproc_functions = ( self.__functions_are_preprocessed and from_original_functions ) if not eval_obj: functions = [] elif use_nonproc_functions: functions = [self.nonproc_objective] else: functions = [self.objective] if constraint_names is not None: for name in constraint_names: functions.append(self.__get_constraint(name, from_original_functions)) elif use_nonproc_functions: functions += self.nonproc_constraints else: functions += self.constraints if observable_names is not None: for name in observable_names: functions.append(self.__get_observable(name, from_original_functions)) elif eval_observables and use_nonproc_functions: functions += self.nonproc_observables elif eval_observables: functions += self.observables return functions
[docs] def preprocess_functions( self, is_function_input_normalized: bool = True, use_database: bool = True, round_ints: bool = True, eval_obs_jac: bool = False, support_sparse_jacobian: bool = False, ) -> None: """Pre-process all the functions and eventually the gradient. Required to wrap the objective function and constraints with the database and eventually the gradients by complex step or finite differences. Args: is_function_input_normalized: Whether to consider the function input as normalized and unnormalize it before the evaluation takes place. use_database: Whether to wrap the functions in the database. round_ints: Whether to round the integer variables. eval_obs_jac: Whether to evaluate the Jacobian of the observables. support_sparse_jacobian: Whether the driver support sparse Jacobian. """ if round_ints: # Keep the rounding option only if there is an integer design variable round_ints = any( np_any(var_type == DesignSpace.DesignVariableType.INTEGER) for var_type in self.design_space.variable_types.values() ) # Avoids multiple wrappings of functions when multiple executions # are performed, in bi-level scenarios for instance if not self.__functions_are_preprocessed: self.preprocess_options = { "is_function_input_normalized": is_function_input_normalized, "use_database": use_database, "round_ints": round_ints, } # Preprocess the constraints for icstr, cstr in enumerate(self.constraints): self.nonproc_constraints.append(cstr) p_cstr = self.__preprocess_func( cstr, is_function_input_normalized=is_function_input_normalized, use_database=use_database, round_ints=round_ints, support_sparse_jacobian=support_sparse_jacobian, ) p_cstr.special_repr = cstr.special_repr self.constraints[icstr] = p_cstr # Preprocess the observables for iobs, obs in enumerate(self.observables): self.nonproc_observables.append(obs) p_obs = self.__preprocess_func( obs, is_function_input_normalized=is_function_input_normalized, use_database=use_database, round_ints=round_ints, is_observable=True, support_sparse_jacobian=support_sparse_jacobian, ) p_obs.special_repr = obs.special_repr self.observables[iobs] = p_obs for iobs, obs in enumerate(self.new_iter_observables): self.nonproc_new_iter_observables.append(obs) p_obs = self.__preprocess_func( obs, is_function_input_normalized=False, use_database=use_database, round_ints=round_ints, is_observable=True, support_sparse_jacobian=support_sparse_jacobian, ) p_obs.special_repr = obs.special_repr self.new_iter_observables[iobs] = p_obs # Preprocess the objective self.nonproc_objective = self.objective self._objective = self.__preprocess_func( self.objective, is_function_input_normalized=is_function_input_normalized, use_database=use_database, round_ints=round_ints, support_sparse_jacobian=support_sparse_jacobian, ) self._objective.special_repr = self.objective.special_repr self.__functions_are_preprocessed = True self.check() self.__eval_obs_jac = eval_obs_jac
[docs] def execute_observables_callback(self, last_x: ndarray) -> None: """The callback function to be passed to the database. Call all the observables with the last design variables values as argument. Args: last_x: The design variables values from the last evaluation. """ for new_iter_observable in self.new_iter_observables: new_iter_observable(last_x) if self.__eval_obs_jac: new_iter_observable.jac(last_x)
def __preprocess_func( self, func: MDOFunction, is_function_input_normalized: bool = True, use_database: bool = True, round_ints: bool = True, is_observable: bool = False, support_sparse_jacobian: bool = False, ) -> MDOFunction: """Wrap the function to differentiate it and store its call in the database. Only the computed gradients are stored in the database, not the eventual finite differences or complex step perturbed evaluations. Args: func: The scaled and derived function to be pre-processed. is_function_input_normalized: Whether to consider the function input as normalized and unnormalize it before the evaluation takes place. use_database: If ``True``, then the function is wrapped in the database. round_ints: If ``True``, then round the integer variables. is_observable: If ``True``, new_iter_listeners are not called when function is called (avoid recursive call) support_sparse_jacobian: Whether the driver support sparse Jacobian. Returns: The pre-processed function. """ self.check_format(func) # First differentiate it so that the finite differences evaluations # are not stored in the database, which would be the case in the other # way round # Also, store non normalized values in the database for further # exploitation # Convert Jacobian in dense format if needed if not support_sparse_jacobian: func = DenseJacobianFunction(func) if ( isinstance(func, MDOLinearFunction) and not round_ints and is_function_input_normalized ): func = self.__normalize_linear_function(func) else: func = NormFunction(func, is_function_input_normalized, round_ints, self) if self.differentiation_method in set(self.ApproximationMode): self.__add_approximated_jac_function(func, is_function_input_normalized) # Cast to real value, the results can be a complex number (ComplexStep) if use_database: func = NormDBFunction( func, is_function_input_normalized, is_observable, self ) return func def __normalize_linear_function( self, orig_func: MDOLinearFunction, ) -> MDOLinearFunction: """Create a linear function using a scaled input vector. Args: orig_func: The original linear function Returns: The scaled linear function. Raises: TypeError: If the original function is not an :class:`.MDOLinearFunction`. """ if not isinstance(orig_func, MDOLinearFunction): msg = "Original function must be linear" raise TypeError(msg) design_space = self.design_space # Get normalization factors and shift norm_policies = design_space.dict_to_array(design_space.normalize) norm_factors = where( norm_policies, design_space.get_upper_bounds() - design_space.get_lower_bounds(), 1.0, ) shift = where(norm_policies, design_space.get_lower_bounds(), 0.0) # Build the normalized linear function if isinstance(orig_func.coefficients, sparse_classes): coefficients = deepcopy(orig_func.coefficients) coefficients.data *= norm_factors[coefficients.indices] else: coefficients = multiply(orig_func.coefficients, norm_factors) value_at_zero = orig_func(shift) return MDOLinearFunction( coefficients, orig_func.name, orig_func.f_type, orig_func.input_names, value_at_zero, ) def __add_approximated_jac_function( self, func: MDOFunction, normalize: bool, ) -> None: """Define the Jacobian function of an :class:`MDOFunction` as an approximator. Args: func: The function of interest. normalize: Whether to unnormalize the input vector of the function before evaluate it. """ if self.differentiation_method not in set(self.ApproximationMode): return differentiation_object = GradientApproximatorFactory().create( self.differentiation_method, func.evaluate, step=self.fd_step, design_space=self.design_space, normalize=normalize, parallel=self.__parallel_differentiation, **self.__parallel_differentiation_options, ) func.jac = differentiation_object.f_gradient
[docs] def check(self) -> None: """Check if the optimization problem is ready for run. Raises: ValueError: If the objective function is missing. """ if self.objective is None: msg = "Missing objective function in OptimizationProblem" raise ValueError(msg) self.design_space.check() self.__check_differentiation_method() self.check_format(self.objective) self.__check_functions()
def __check_functions(self) -> None: """Check that the constraints are well declared. Raises: ValueError: If a function declared as a constraint has the wrong type. """ for cstr in self.constraints: self.check_format(cstr) if not cstr.is_constraint(): msg = ( f"Constraint type is not eq or ineq !, got {cstr.f_type}" " instead " ) raise ValueError(msg) self.check_format(self.objective) def __check_differentiation_method(self) -> None: """Check that the differentiation method is in allowed ones. Available ones are: :attr:`.OptimizationProblem.DifferentiationMethod`. Raises: ValueError: If either the differentiation method is unknown, the complex step is null or the finite differences' step is null. """ if self.differentiation_method == self.ApproximationMode.COMPLEX_STEP: if self.fd_step == 0: msg = "ComplexStep step is null!" raise ValueError(msg) if self.fd_step.imag != 0: LOGGER.warning( "Complex step method has an imaginary " "step while required a pure real one." " Auto setting the real part" ) self.fd_step = self.fd_step.imag elif self.differentiation_method == self.ApproximationMode.FINITE_DIFFERENCES: if self.fd_step == 0: msg = "Finite differences step is null!" raise ValueError(msg) if self.fd_step.imag != 0: LOGGER.warning( "Finite differences method has a complex " "step while required a pure real one." " Auto setting the imaginary part to 0" ) self.fd_step = self.fd_step.real # TODO: API: to be deprecated in favor of self.minimize_objective
[docs] def change_objective_sign(self) -> None: """Change the objective function sign in order to minimize its opposite. The :class:`.OptimizationProblem` expresses any optimization problem as a minimization problem. Then, an objective function originally expressed as a performance function to maximize must be converted into a cost function to minimize, by means of this method. """ self.__minimize_objective = not self.__minimize_objective self.objective = -self.objective
def _satisfied_constraint( self, cstr_type: MDOFunction.ConstraintType, value: ndarray, ) -> bool: """Determine if an evaluation satisfies a constraint within a given tolerance. Args: cstr_type: The type of the constraint. value: The value of the constraint. Returns: Whether a value satisfies a constraint. """ if cstr_type == MDOFunction.ConstraintType.EQ: return np_all(np_abs(value) <= self.eq_tolerance) return np_all(value <= self.ineq_tolerance)
[docs] def is_point_feasible( self, out_val: dict[str, ndarray], constraints: Iterable[MDOFunction] | None = None, ) -> bool: """Check if a point is feasible. Notes: If the value of a constraint is absent from this point, then this constraint will be considered satisfied. Args: out_val: The values of the objective function, and eventually constraints. constraints: The constraints whose values are to be tested. If ``None``, then take all constraints of the problem. Returns: The feasibility of the point. """ if constraints is None: constraints = self.get_ineq_constraints() + self.get_eq_constraints() feasible = True for constraint in constraints: # look for the evaluation of the constraint eval_cstr = out_val.get(constraint.name, None) # if evaluation exists, check if it is satisfied if eval_cstr is None or not self._satisfied_constraint( constraint.f_type, eval_cstr ): feasible = False break return feasible
[docs] def get_feasible_points( self, ) -> tuple[list[ndarray], list[dict[str, float | list[int]]]]: """Retrieve the feasible points within a given tolerance. This tolerance is defined by :attr:`.OptimizationProblem.eq_tolerance` for equality constraints and :attr:`.OptimizationProblem.ineq_tolerance` for inequality ones. Returns: The values of the design variables and objective function for the feasible points. """ x_history = [] f_history = [] constraints = self.get_ineq_constraints() + self.get_eq_constraints() for x_vect, out_val in self.database.items(): feasible = self.is_point_feasible(out_val, constraints=constraints) # if all constraints are satisfied, store the vector if feasible: x_history.append(x_vect.unwrap()) f_history.append(out_val) return x_history, f_history
# TODO: API: rename to check_design_point_is_feasible
[docs] def get_violation_criteria( self, x_vect: ndarray, ) -> tuple[bool, float]: r"""Check if a design point is feasible and measure its constraint violation. The constraint violation measure at a design point :math:`x` is .. math:: \lVert\max(g(x)-\varepsilon_{\text{ineq}},0)\rVert_2^2 +\lVert|\max(|h(x)|-\varepsilon_{\text{eq}},0)\rVert_2^2 where :math:`\|.\|_2` is the Euclidean norm, :math:`g(x)` is the inequality constraint vector, :math:`h(x)` is the equality constraint vector, :math:`\varepsilon_{\text{ineq}}` is the tolerance for the inequality constraints and :math:`\varepsilon_{\text{eq}}` is the tolerance for the equality constraints. If the design point is feasible, the constraint violation measure is 0. Args: x_vect: The design point :math:`x`. Returns: Whether the design point is feasible, and its constraint violation measure. """ violation = 0.0 x_vect_is_feasible = True output_names_to_values = self.database.get(x_vect) for constraint in self.constraints: constraint_value = output_names_to_values.get(constraint.name) if constraint_value is None: break f_type = constraint.f_type if self._satisfied_constraint(f_type, constraint_value): continue x_vect_is_feasible = False if isnan(constraint_value).any(): return x_vect_is_feasible, inf if f_type == MDOFunction.ConstraintType.INEQ: tolerance = self.ineq_tolerance else: tolerance = self.eq_tolerance constraint_value = abs(constraint_value) if isinstance(constraint_value, ndarray): violated_components = (constraint_value > tolerance).nonzero() constraint_value = constraint_value[violated_components] violation += norm(constraint_value - tolerance) ** 2 return x_vect_is_feasible, violation
[docs] def get_best_infeasible_point( self, ) -> BestInfeasiblePointType: """Retrieve the best infeasible point within a given tolerance. Returns: The best infeasible point expressed as the design variables values, the objective function value, the feasibility of the point and the functions values. """ x_history = [] f_history = [] is_feasible = [] viol_criteria = [] for x_vect, out_val in self.database.items(): is_pt_feasible, f_violation = self.get_violation_criteria(x_vect) is_feasible.append(is_pt_feasible) viol_criteria.append(f_violation) x_history.append(x_vect.unwrap()) f_history.append(out_val) is_opt_feasible = False if viol_criteria: best_i = int(argmin(array(viol_criteria))) is_opt_feasible = is_feasible[best_i] else: best_i = 0 if len(f_history) <= best_i: outputs_opt = {} x_opt = None f_opt = None else: outputs_opt = f_history[best_i] x_opt = x_history[best_i] f_opt = outputs_opt.get(self.objective.name) if isinstance(f_opt, ndarray) and len(f_opt) == 1: f_opt = f_opt[0] return x_opt, f_opt, is_opt_feasible, outputs_opt
def __get_optimum_infeas( self, ) -> OptimumSolutionType: """Retrieve the optimum solution. Use a feasibility tolerance, when there is no feasible point. Returns: The optimum solution expressed by the design variables values, the objective function value, the constraints values and the constraints gradients values. """ msg = ( "Optimization found no feasible point ! " " The least infeasible point is selected." ) LOGGER.warning(msg) x_opt, f_opt, _, f_history = self.get_best_infeasible_point() c_opt = {} c_opt_grad = {} constraints = self.get_ineq_constraints() + self.get_eq_constraints() for constraint in constraints: c_opt[constraint.name] = f_history.get(constraint.name) f_key = Database.get_gradient_name(constraint.name) c_opt_grad[constraint.name] = f_history.get(f_key) return x_opt, f_opt, c_opt, c_opt_grad def __get_optimum_feas( self, feas_x: Sequence[ndarray], feas_f: Sequence[dict[str, float | list[int]]], ) -> OptimumSolutionType: """Retrieve the optimum solution. Use a feasibility tolerances, when there is a feasible point. Args: feas_x: The values of the design parameters for the feasible evaluations. feas_f: The values of the functions for the feasible evaluations. Returns: The optimum solution expressed by the design variables values, the objective function value, the constraints values and the constraints gradients values. """ f_opt, x_opt = inf, None c_opt = {} c_opt_grad = {} obj_name = self.objective.name constraints = self.get_ineq_constraints() + self.get_eq_constraints() for i, out_val in enumerate(feas_f): obj_eval = out_val.get(obj_name) if obj_eval is None or isinstance(obj_eval, Number) or obj_eval.size == 1: tmp_objeval = obj_eval else: tmp_objeval = norm(obj_eval) if tmp_objeval is not None and tmp_objeval < f_opt: f_opt = tmp_objeval x_opt = feas_x[i] for constraint in constraints: c_name = constraint.name c_opt[c_name] = feas_f[i].get(c_name) c_key = Database.get_gradient_name(c_name) c_opt_grad[constraint.name] = feas_f[i].get(c_key) if isinstance(f_opt, ndarray) and len(f_opt) == 1: f_opt = f_opt[0] return x_opt, f_opt, c_opt, c_opt_grad
[docs] def get_optimum(self) -> OptimumType: """Return the optimum solution within a given feasibility tolerances. Returns: The optimum result, defined by: - the value of the objective function, - the value of the design variables, - the indicator of feasibility of the optimal solution, - the value of the constraints, - the value of the gradients of the constraints. Raises: ValueError: When the optimization database is empty. """ if not self.database: msg = "Optimization history is empty" raise ValueError(msg) feas_x, feas_f = self.get_feasible_points() if not feas_x: is_feas = False x_opt, f_opt, c_opt, c_opt_d = self.__get_optimum_infeas() else: is_feas = True x_opt, f_opt, c_opt, c_opt_d = self.__get_optimum_feas(feas_x, feas_f) return f_opt, x_opt, is_feas, c_opt, c_opt_d
[docs] def get_last_point(self) -> OptimumType: """Return the last design point. Returns: The last point result, defined by: - the value of the objective function, - the value of the design variables, - the indicator of feasibility of the last point, - the value of the constraints, - the value of the gradients of the constraints. Raises: ValueError: When the optimization database is empty. """ if not self.database: msg = "Optimization history is empty" raise ValueError(msg) x_last = self.database.get_x_vect(-1) f_last = self.database.get_function_value(self.objective.name, -1) is_feas = self.is_point_feasible(self.database[x_last], self.constraints) c_last = {} c_last_grad = {} for constraint in self.constraints: c_last[constraint.name] = self.database[x_last].get(constraint.name) f_key = Database.get_gradient_name(constraint.name) c_last_grad[constraint.name] = self.database[x_last].get(f_key) return f_last, x_last, is_feas, c_last, c_last_grad
@property def __string_representation(self) -> MultiLineString: """The string representation of the optimization problem.""" mls = MultiLineString() mls.add("Optimization problem:") mls.indent() # objective representation if self.minimize_objective or self.use_standardized_objective: optimize_verb = "minimize " start = 0 else: optimize_verb = "maximize " start = 1 objective_function = [line for line in repr(self.objective).split("\n") if line] mls.add(optimize_verb + objective_function[0][start:]) for line in objective_function[1:]: mls.add(" " * len(optimize_verb) + line) # variables representation mls.add("with respect to {}", pretty_str(self.design_space.variable_names)) if self.has_constraints(): mls.add("subject to constraints:") mls.indent() for constraints in self.get_ineq_constraints(): constraints = [cstr for cstr in str(constraints).split("\n") if cstr] for constraint in constraints: mls.add(constraint) for constraints in self.get_eq_constraints(): constraints = [cstr for cstr in str(constraints).split("\n") if cstr] for constraint in constraints: mls.add(constraint) return mls def __repr__(self) -> str: return str(self.__string_representation) def _repr_html_(self) -> str: return self.__string_representation._repr_html_() @staticmethod def __store_h5data( group: Any, data_array: ndarray[Number] | str | list[str | Number], dataset_name: str, dtype: str | None = None, ) -> None: """Store an array in a hdf5 file group. Args: group: The group pointer. data_array: The data to be stored. dataset_name: The name of the dataset to store the array. dtype: Numpy dtype or string. If ``None``, dtype('f') will be used. """ if data_array is None or ( isinstance(data_array, Iterable) and not len(data_array) ): return if isinstance(data_array, ndarray): data_array = data_array.real if isinstance(data_array, str): data_array = array([data_array], dtype="bytes") if isinstance(data_array, list): all_str = reduce( lambda x, y: x or y, (isinstance(data, str) for data in data_array), ) if all_str: data_array = array([data_array], dtype="bytes") dtype = data_array.dtype group.create_dataset(dataset_name, data=data_array, dtype=dtype) @classmethod def __store_attr_h5data(cls, obj: Any, group: Group) -> None: """Store an object in the HDF5 dataset. The object shall be a mapping or have a method to_dict(). Args: obj: The object to store group: The hdf5 group. """ data = obj if isinstance(obj, Mapping) else obj.to_dict() for name, value in data.items(): dtype = None if isinstance(value, str): value = value.encode("ascii", "ignore") elif isinstance(value, bytes): value = value.decode() elif isinstance(value, Mapping) and not isinstance(value, DesignSpace): grname = f"/{name}" if grname in group: del group[grname] new_group = group.require_group(grname) cls.__store_attr_h5data(value, new_group) continue elif hasattr(value, "__iter__") and not ( isinstance(value, ndarray) and issubdtype(value.dtype, np_number) ): value = [ att.encode("ascii", "ignore") if isinstance(att, str) else att for att in value ] dtype = h5py.special_dtype(vlen=str) cls.__store_h5data(group, value, name, dtype)
[docs] def to_hdf( self, file_path: str | Path, append: bool = False, hdf_node_path: str = "", ) -> None: """Export the optimization problem to an HDF file. Args: file_path: The path of the file to store the data. append: If ``True``, then the data are appended to the file if not empty. hdf_node_path: The path of the HDF node in which the database should be exported. If empty, the root node is considered. """ LOGGER.info( "Exporting the optimization problem to the file %s at node %s", str(file_path), str(hdf_node_path), ) mode = "a" if append else "w" with h5py.File(file_path, mode) as h5file: if hdf_node_path: h5file = h5file.require_group(hdf_node_path) no_design_space = DesignSpace.DESIGN_SPACE_GROUP not in h5file if not append or self.OPT_DESCR_GROUP not in h5file: opt_group = h5file.require_group(self.OPT_DESCR_GROUP) for attr_name in self.OPTIM_DESCRIPTION: attr = getattr(self, attr_name) self.__store_h5data(opt_group, attr, attr_name) obj_group = h5file.require_group(self.OBJECTIVE_GROUP) self.__store_attr_h5data(self.objective, obj_group) if self.constraints: constraint_group = h5file.require_group(self.CONSTRAINTS_GROUP) for constraint in self.constraints: c_subgroup = constraint_group.require_group(constraint.name) self.__store_attr_h5data(constraint, c_subgroup) if self.observables: observables_group = h5file.require_group(self.OBSERVABLES_GROUP) for observable in self.observables: o_subgroup = observables_group.require_group(observable.name) self.__store_attr_h5data(observable, o_subgroup) if hasattr(self.solution, "to_dict"): # TODO: replace by "if self.solution is None" sol_group = h5file.require_group(self.SOLUTION_GROUP) self.__store_attr_h5data(self.solution, sol_group) self.database.to_hdf(file_path, append=True, hdf_node_path=hdf_node_path) # Design space shall remain the same in append mode if not append or no_design_space: self.design_space.to_hdf( file_path, append=True, hdf_node_path=hdf_node_path )
[docs] @classmethod def from_hdf( cls, file_path: str | Path, x_tolerance: float = 0.0, hdf_node_path: str = "", ) -> OptimizationProblem: """Import an optimization history from an HDF file. Args: file_path: The file containing the optimization history. x_tolerance: The tolerance on the design variables when reading the file. hdf_node_path: The path of the HDF node from which the database should be imported. If empty, the root node is considered. Returns: The read optimization problem. """ LOGGER.info( "Importing the optimization problem from the file %s at node %s", file_path, hdf_node_path, ) design_space = DesignSpace.from_file(file_path, hdf_node_path=hdf_node_path) opt_pb = OptimizationProblem( design_space, input_database=file_path, hdf_node_path=hdf_node_path ) with h5py.File(file_path) as h5file: h5file = get_hdf5_group(h5file, hdf_node_path) if opt_pb.SOLUTION_GROUP in h5file: group_data = cls.__h5_group_to_dict(h5file, opt_pb.SOLUTION_GROUP) if "x_0_as_dict" in h5file: group_data["x_0_as_dict"] = cls.__h5_group_to_dict( h5file, "x_0_as_dict" ) if "x_opt_as_dict" in h5file: group_data["x_opt_as_dict"] = cls.__h5_group_to_dict( h5file, "x_opt_as_dict" ) attr = OptimizationResult.from_dict(group_data) opt_pb.solution = attr group_data = cls.__h5_group_to_dict(h5file, opt_pb.OBJECTIVE_GROUP) attr = MDOFunction.init_from_dict_repr(**group_data) # The generated functions can be called at the x stored in # the database attr.set_pt_from_database( opt_pb.database, design_space, x_tolerance=x_tolerance ) opt_pb.objective = attr group = get_hdf5_group(h5file, opt_pb.OPT_DESCR_GROUP) for attr_name, attr in group.items(): val = attr[()] if isinstance(val, ndarray) and isinstance(val[0], bytes_): val = val[0].decode() # Set the private attribute __minimize_objective instead of the property # to avoid an unnecessary change of sign of the objective function. if attr_name == "minimize_objective": attr_name = "_OptimizationProblem__minimize_objective" setattr(opt_pb, attr_name, val) if opt_pb.CONSTRAINTS_GROUP in h5file: group = get_hdf5_group(h5file, opt_pb.CONSTRAINTS_GROUP) for cstr_name in group: group_data = cls.__h5_group_to_dict(group, cstr_name) attr = MDOFunction.init_from_dict_repr(**group_data) opt_pb.constraints.append(attr) if opt_pb.OBSERVABLES_GROUP in h5file: group = get_hdf5_group(h5file, opt_pb.OBSERVABLES_GROUP) for observable_name in group: group_data = cls.__h5_group_to_dict(group, observable_name) attr = MDOFunction.init_from_dict_repr(**group_data) opt_pb.observables.append(attr) is_mono_objective = False with contextlib.suppress(ValueError): # Sometimes the dimension of the problem cannot be determined. is_mono_objective = opt_pb.is_mono_objective if not is_mono_objective and opt_pb.SOLUTION_GROUP in h5file: pareto_front = ( ParetoFront.from_optimization_problem(opt_pb) if opt_pb.solution.is_feasible else None ) opt_pb.solution = MultiObjectiveOptimizationResult( **opt_pb.solution.__dict__, pareto_front=pareto_front ) return opt_pb
[docs] def to_dataset( self, name: str = "", categorize: bool = True, opt_naming: bool = True, export_gradients: bool = False, input_values: Iterable[ndarray] = (), ) -> Dataset: """Export the database of the optimization problem to a :class:`.Dataset`. The variables can be classified into groups: :attr:`.Dataset.DESIGN_GROUP` or :attr:`.Dataset.INPUT_GROUP` for the design variables and :attr:`.Dataset.FUNCTION_GROUP` or :attr:`.Dataset.OUTPUT_GROUP` for the functions (objective, constraints and observables). Args: name: The name to be given to the dataset. If empty, use the name of the :attr:`.OptimizationProblem.database`. categorize: Whether to distinguish between the different groups of variables. Otherwise, group all the variables in :attr:`.Dataset.PARAMETER_GROUP``. opt_naming: Whether to use :attr:`.Dataset.DESIGN_GROUP` and :attr:`.Dataset.FUNCTION_GROUP` as groups. Otherwise, use :attr:`.Dataset.INPUT_GROUP` and :attr:`.Dataset.OUTPUT_GROUP`. export_gradients: Whether to export the gradients of the functions (objective function, constraints and observables) if the latter are available in the database of the optimization problem. input_values: The input values to be considered. If empty, consider all the input values of the database. Returns: A dataset built from the database of the optimization problem. """ dataset_name = name or self.database.name # Set the different groups if categorize: if opt_naming: dataset_class = OptimizationDataset input_group = OptimizationDataset.DESIGN_GROUP output_group = OptimizationDataset.FUNCTION_GROUP gradient_group = OptimizationDataset.GRADIENT_GROUP else: dataset_class = IODataset input_group = IODataset.INPUT_GROUP output_group = IODataset.OUTPUT_GROUP gradient_group = IODataset.GRADIENT_GROUP else: dataset_class = Dataset input_group = output_group = gradient_group = Dataset.DEFAULT_GROUP # Add database inputs input_names = self.design_space.variable_names names_to_sizes = self.design_space.variable_sizes input_history = array(self.database.get_x_vect_history()) n_samples = len(input_history) positions = [] offset = int(categorize & opt_naming) for input_value in input_values: _positions = ((input_history == input_value).all(axis=1)).nonzero()[0] positions.extend((_positions + offset).tolist()) data = [input_history.real] columns = [ (input_group, name, index) for name in input_names for index in range(names_to_sizes[name]) ] # Add database outputs variable_names = self.database.get_function_names() output_names = [name for name in variable_names if name not in input_names] self.__add_data_to_database( data, columns, output_names, n_samples, output_group, False ) # Add database output gradients if export_gradients: self.__add_data_to_database( data, columns, output_names, n_samples, gradient_group, True ) return dataset_class( hstack(data), dataset_name=dataset_name, columns=MultiIndex.from_tuples( columns, names=dataset_class.COLUMN_LEVEL_NAMES, ), ).get_view(indices=positions)
def __add_data_to_database( self, data: list[NDArray[float]], columns: list[tuple[str, str, int]], output_names: Iterable[str], n_samples: int, group: str, store_gradient: bool, ) -> None: """Add the database output gradients to the dataset. Args: data: The sequence of data arrays to be augmented with the output data. columns: The multi-index columns to be augmented with the output names. output_names: The names of the outputs in the database. n_samples: The total number of samples, including possible points where the evaluation failed. group: The dataset group where the variables will be added. store_gradient: Whether the variable of interest is the gradient of the output. """ x_vect_history = array(self.database.get_x_vect_history()) for output_name in output_names: if store_gradient: function_name = Database.get_gradient_name(output_name) if self.database.check_output_history_is_empty(function_name): continue else: function_name = output_name history, input_history = self.database.get_function_history( function_name=function_name, with_x_vect=True ) history = ( self.__replace_missing_values( history, input_history, x_vect_history, ) .reshape((n_samples, -1)) .real ) data.append(history) columns.extend([(group, function_name, i) for i in range(history.shape[1])]) @staticmethod def __replace_missing_values( output_history: ndarray, input_history: ndarray, full_input_history: ndarray, ) -> ndarray: """Replace the missing output values with NaN. Args: output_history: The output data history with possibly missing values. input_history: The input data history with possibly missing values. full_input_history: The complete input data history, with no missing values. Returns: The output data history where missing values have been replaced with NaN. """ database_size = full_input_history.shape[0] if len(input_history) != database_size: # There are fewer entries than in the full input history. # Add NaN values at the missing input data. # N.B. the input data are assumed to be in the same order. index = 0 for input_data in input_history: while not array_equal(input_data, full_input_history[index]): output_history = insert(output_history, index, nan, 0) index += 1 index += 1 return insert(output_history, [index] * (database_size - index), nan, 0) return output_history @staticmethod def __h5_group_to_dict( h5_handle: h5py.File | h5py.Group, group_name: str, ) -> dict[str, str | list[str]]: """Convert the values of a hdf5 dataset. Values that are of the kind string or bytes are converted to string or list of strings. Args: h5_handle: A hdf5 file or group. group_name: The name of the group to be converted. Returns: The converted dataset. """ converted = {} group = get_hdf5_group(h5_handle, group_name) for key, value in group.items(): value = value[()] # h5py does not handle bytes natively, it maps it to a numpy generic type if isinstance(value, ndarray) and value.dtype.type in { numpy.object_, bytes_, }: value = value[0] if value.size == 1 else value.tolist() if isinstance(value, bytes): value = value.decode() if isinstance(value, list): value = [ sub_value.decode() if isinstance(sub_value, bytes) else sub_value for sub_value in value ] converted[key] = value return converted
[docs] def get_data_by_names( self, names: str | Iterable[str], as_dict: bool = True, filter_non_feasible: bool = False, ) -> ndarray | dict[str, ndarray]: """Return the data for specific names of variables. Args: names: The names of the variables. as_dict: If ``True``, return values as dictionary. filter_non_feasible: If ``True``, remove the non-feasible points from the data. Returns: The data related to the variables. """ dataset = self.to_dataset("OptimizationProblem") if as_dict: data = dataset.get_view(variable_names=names).to_dict(orient="list") else: data = dataset.get_view(variable_names=names).to_numpy() if filter_non_feasible: x_feasible, _ = self.get_feasible_points() feasible_indexes = [self.database.get_iteration(x) - 1 for x in x_feasible] if as_dict: for key, value in data.items(): data[key] = array(value)[feasible_indexes] else: data = data[feasible_indexes, :] return data
@property def is_mono_objective(self) -> bool: """Whether the optimization problem is mono-objective. Raises: ValueError: When the dimension of the objective cannot be determined. """ obj_dim = self.objective.dim if obj_dim != 0: return obj_dim == 1 n_outvars = len(self.objective.output_names) if n_outvars == 0: msg = "Cannot determine the dimension of the objective." raise ValueError(msg) return n_outvars == 1
[docs] def get_functions_dimensions( self, names: Iterable[str] | None = None ) -> dict[str, int]: """Return the dimensions of the outputs of the problem functions. Args: names: The names of the functions. If ``None``, then the objective and all the constraints are considered. Returns: The dimensions of the outputs of the problem functions. The dictionary keys are the functions names and the values are the functions dimensions. """ if names is None: names = [self.objective.name, *self.get_constraint_names()] return {name: self.get_function_dimension(name) for name in names}
[docs] def get_function_dimension(self, name: str) -> int: """Return the dimension of a function of the problem (e.g. a constraint). Args: name: The name of the function. Returns: The dimension of the function. Raises: ValueError: If the function name is unknown to the problem. RuntimeError: If the function dimension is not unavailable. """ # Check that the required function belongs to the problem and get it for func in self.get_all_functions(): if func.name == name: function = func break else: msg = f"The problem has no function named {name}." raise ValueError(msg) # Get the dimension of the function output if function.dim: return function.dim if self.design_space.has_current_value(): if function.expects_normalized_inputs: current_variables = self.get_x0_normalized() else: current_variables = self.design_space.get_current_value() return atleast_1d(function(current_variables)).size msg = f"The output dimension of function {name} is not available." raise RuntimeError(msg)
[docs] def get_number_of_unsatisfied_constraints( self, design_variables: ndarray, values: Mapping[str, float | ndarray] = READ_ONLY_EMPTY_DICT, ) -> int: """Return the number of scalar constraints not satisfied by design variables. Args: design_variables: The design variables. values: The values of the constraints. N.B. the missing values will be read from the database or computed. Returns: The number of unsatisfied scalar constraints. """ n_unsatisfied = 0 missing_names = set(self.get_constraint_names()).difference(values) if missing_names: constraints_values = self.evaluate_functions( design_variables, eval_obj=False, eval_observables=False, normalize=False, constraint_names=missing_names, )[0] constraints_values.update(values) else: constraints_values = values for constraint in self.constraints: value = atleast_1d(constraints_values[constraint.name]) if constraint.f_type == MDOFunction.ConstraintType.EQ: value = numpy.absolute(value) tolerance = self.eq_tolerance else: tolerance = self.ineq_tolerance n_unsatisfied += sum(value > tolerance) return n_unsatisfied
[docs] def get_scalar_constraint_names(self) -> list[str]: """Return the names of the scalar constraints. Returns: The names of the scalar constraints. """ constraint_names = [] for constraint in self.constraints: dimension = self.get_function_dimension(constraint.name) if dimension == 1: constraint_names.append(constraint.name) else: constraint_names.extend([ constraint.get_indexed_name(index) for index in range(dimension) ]) return constraint_names
[docs] def reset( self, database: bool = True, current_iter: bool = True, design_space: bool = True, function_calls: bool = True, preprocessing: bool = True, ) -> None: """Partially or fully reset the optimization problem. Args: database: Whether to clear the database. current_iter: Whether to reset the current iteration :attr:`.OptimizationProblem.current_iter`. design_space: Whether to reset the current point of the :attr:`.OptimizationProblem.design_space` to its initial value (possibly none). function_calls: Whether to reset the number of calls of the functions. preprocessing: Whether to turn the pre-processing of functions to False. """ if current_iter: self.current_iter = 0 if database: self.database.clear() if design_space: self.design_space.set_current_value(self.__initial_current_x) if function_calls and MDOFunction.activate_counters: for func in self.get_all_functions(): func.n_calls = 0 if self.__functions_are_preprocessed: for original_functions in self.get_all_functions(True): original_functions.n_calls = 0 if preprocessing and self.__functions_are_preprocessed: self.objective = self.nonproc_objective self.nonproc_objective = None self.constraints = self.nonproc_constraints self.nonproc_constraints = [] self.observables = self.nonproc_observables self.nonproc_observables = [] self.new_iter_observables = self.nonproc_new_iter_observables self.nonproc_new_iter_observables = [] self.__functions_are_preprocessed = False
def __get_constraint( self, name: str, from_original_constraints: bool = False ) -> MDOFunction: """Return a constraint of the problem. Args: name: The name of the constraint. from_original_constraints: Whether to get the constraint from the original constraints; otherwise get the constraint from the pre-processed constraints. Returns: The constraint. """ return self.__get_function( name, from_original_constraints, self.get_constraint_names(), self.nonproc_constraints, self.constraints, self.CONSTRAINTS_GROUP, ) def __get_function( self, name: str, from_original_functions: bool, names: Iterable[str], original_functions: Iterable[MDOFunction], preprocessed_functions: Iterable[MDOFunction], group_name: str, ) -> MDOFunction: """Return a function of the problem. Args: name: The name of the function. from_original_functions: Whether to get the function from the original functions; otherwise get the function for the pre-processed functions. names: The names of the available functions. original_functions: The original functions. preprocessed_functions: The pre-processed functions. group_name: The name of the group of functions. Returns: The function. Raises: ValueError: If the name is not among the names of the available functions. """ if name not in names: msg = ( f"{name} is not among the names of the {group_name}: " f"{pretty_str(names)}." ) raise ValueError(msg) if from_original_functions and self.__functions_are_preprocessed: functions = original_functions else: functions = preprocessed_functions return next(function for function in functions if function.name == name)