# 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
"""Base class for optimization history post-processing."""
from __future__ import annotations
from abc import abstractmethod
from collections.abc import Iterable
from collections.abc import Sequence
from typing import TYPE_CHECKING
from typing import Any
from typing import ClassVar
from typing import Generic
from typing import TypeVar
from typing import Union
from gemseo.post.base_post_settings import BasePostSettings
from gemseo.post.dataset.dataset_plot import DatasetPlot
from gemseo.utils.file_path_manager import FilePathManager
from gemseo.utils.matplotlib_figure import FigSizeType
from gemseo.utils.matplotlib_figure import save_show_figure
from gemseo.utils.metaclasses import ABCGoogleDocstringInheritanceMeta
from gemseo.utils.pydantic import create_model
from gemseo.utils.string_tools import repr_variable
if TYPE_CHECKING:
from pathlib import Path
from matplotlib.figure import Figure
from gemseo.algos.database import Database
from gemseo.algos.optimization_problem import OptimizationProblem
BasePostOptionType = Union[int, float, str, bool, Sequence[str], FigSizeType]
T = TypeVar("T", bound=BasePostSettings)
[docs]
class BasePost(Generic[T], metaclass=ABCGoogleDocstringInheritanceMeta):
"""Base class for optimization post-processing."""
# Silencing mypy since the root cause does not seem legit,
# and may be changed.
# See https://github.com/python/mypy/issues/5144.
Settings: ClassVar[type[T]]
"""The Pydantic model for the settings."""
optimization_problem: OptimizationProblem
"""The optimization problem."""
database: Database
"""The database generated by the optimization problem."""
materials_for_plotting: dict[Any, Any]
"""The materials to eventually rebuild the plot in another framework."""
_obj_name: str
"""The name of the objective function as passed by the user."""
_standardized_obj_name: str
"""The name of the objective function stored in the database."""
_neg_obj_name: str
"""The name of the objective function starting with a '-'."""
_output_file_paths: list[Path]
"""Paths to the output files."""
__figures: dict[str, Figure | DatasetPlot]
"""The mapping from figure names or nameless figure counters to figures."""
def __init__(
self,
opt_problem: OptimizationProblem,
) -> None:
"""
Args:
opt_problem: The optimization problem to be post-processed.
""" # noqa: D205, D212, D415
self.optimization_problem = opt_problem
self._obj_name = opt_problem.objective_name
self._standardized_obj_name = opt_problem.standardized_objective_name
self._neg_obj_name = f"-{self._obj_name}"
self.database = opt_problem.database
# The data required to eventually rebuild the plot in another framework.
self.materials_for_plotting = {}
default_file_name = FilePathManager.to_snake_case(self.__class__.__name__)
self.__file_path_manager = FilePathManager(
FilePathManager.FileType.FIGURE, default_file_name
)
self._output_file_paths = []
self.__figures = {}
self.__nameless_figure_counter = 0
self._dataset_plots = []
@property
def _change_obj(self) -> bool:
"""Whether to change the objective value and names by using the opposite."""
return not (
self.optimization_problem.minimize_objective
or self.optimization_problem.use_standardized_objective
)
@property
def figures(self) -> dict[str, Figure | DatasetPlot]:
"""The figures indexed by a name, or the nameless figure counter."""
return self.__figures
@property
def output_file_paths(self) -> list[Path]:
"""The paths to the output files."""
return self._output_file_paths
def _add_figure(
self,
figure: Figure | DatasetPlot,
file_name: str = "",
) -> None:
"""Add a figure.
Args:
figure: The figure to be added.
file_name: The default name of the file to save the figure.
If empty, use the nameless figure counter.
"""
if not file_name:
self.__nameless_figure_counter += 1
file_name = str(self.__nameless_figure_counter)
self.__figures[file_name] = figure
[docs]
def execute(
self, settings_model: BasePostSettings | None = None, **settings: Any
) -> dict[str, Figure | DatasetPlot]:
"""Post-process the optimization problem.
Args:
settings_model: The post-processor settings as a Pydantic model.
If ``None``, use ``**settings``.
**settings: The post-processor settings.
This argument is ignored when ``settings_model`` is not ``None``.
Returns:
The figures, to be customized;
in the case of a matplotlib ``Figure``, it must not be closed.
Raises:
ValueError: If the ``opt_problem.database`` is empty.
"""
if not self.optimization_problem.database:
msg = (
f"The post-processor {self.__class__.__name__} cannot be solved "
"because the optimization problem was not solved."
)
raise ValueError(msg)
settings_ = create_model(
self.Settings, settings_model=settings_model, **settings
)
self._plot(settings_)
self.__render(settings_)
return self.__figures
def __render(self, settings: T) -> None:
"""Render the figures.
Args:
settings: The rendering settings.
"""
file_extension = settings.file_extension
file_path = self.__file_path_manager.create_file_path(
file_path=settings.file_path,
directory_path=settings.directory_path,
file_name=settings.file_name,
file_extension=file_extension,
)
file_extension = file_path.suffix[1:]
for figure_name, figure in self.__figures.items():
fig_file_path: str | Path
if settings.save:
if len(self.__figures) > 1:
fig_file_path = self.__file_path_manager.add_suffix(
file_path, figure_name
)
else:
fig_file_path = file_path
self._output_file_paths.append(fig_file_path)
else:
fig_file_path = ""
if isinstance(figure, DatasetPlot):
figure.fig_size = settings.fig_size
figure.execute(
save=settings.save,
show=settings.show,
file_format=file_extension,
file_path=fig_file_path,
)
else:
save_show_figure(
figure, settings.show, fig_file_path, settings.fig_size
)
@abstractmethod
def _plot(self, settings: T) -> None:
"""Create the figures.
Args:
settings: The settings of the post-processor.
"""
def _get_design_variable_names(
self,
variables: Iterable[str] = (),
simplify: bool = False,
) -> list[str]:
"""Create the names of the components of design variables as ``"name[i]"``.
Args:
variables: The design variables of interest.
If empty, use all the design variables.
simplify: Whether to use ``"[i]"`` when ``i>0`` instead of ``"name[i]"``.
Returns:
The names of the components of the design variables.
"""
if not variables:
variables = self.optimization_problem.design_space.variable_names
design_variable_names = []
design_variable_sizes = self.optimization_problem.design_space.variable_sizes
for variable in variables:
design_variable_size = design_variable_sizes[variable]
design_variable_names.extend([
repr_variable(
variable, index, size=design_variable_size, simplify=simplify
)
for index in range(design_variable_size)
])
return design_variable_names
