Source code for gemseo.utils.derivatives.gradient_approximator_factory
# 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.
"""Factory for classes derived from :class:`GradientApproximator`."""
from __future__ import annotations
from typing import TYPE_CHECKING
from typing import Any
from typing import Callable
from gemseo.core.base_factory import BaseFactory
from gemseo.utils.derivatives.gradient_approximator import GradientApproximator
if TYPE_CHECKING:
from numpy import ndarray
from gemseo.algos.design_space import DesignSpace
from gemseo.utils.derivatives.approximation_modes import ApproximationMode
[docs]
class GradientApproximatorFactory(BaseFactory):
"""A factory to create gradient approximators.
In addition to the names of the classes, the factory can be queried with an
:class:`ApproximationMode`.
"""
_CLASS = GradientApproximator
_MODULE_NAMES = ("gemseo.utils.derivatives",)
def __init__(self) -> None: # noqa:D107
super().__init__()
for class_info in tuple(self._names_to_class_info.values()):
approximation_mode = class_info.class_._APPROXIMATION_MODE
self._names_to_class_info[approximation_mode] = class_info
[docs]
def create(
self,
name: str | ApproximationMode,
f_pointer: Callable,
step: float | complex | ndarray | None = None,
design_space: DesignSpace | None = None,
normalize: bool = True,
parallel: bool = False,
**parallel_args: Any,
) -> GradientApproximator:
"""Create a gradient approximator.
Args:
name: The name of the class or the approximation mode.
f_pointer: The pointer to the function to derive.
step: The default differentiation step.
design_space: The design space
containing the upper bounds of the input variables.
If ``None``, consider that the input variables are unbounded.
normalize: Whether to normalize the function.
parallel: Whether to differentiate the function in parallel.
**parallel_args: The parallel execution options,
see :mod:`gemseo.core.parallel_execution`.
Returns:
The gradient approximator.
"""
return super().create(
name,
f_pointer=f_pointer,
step=step,
design_space=design_space,
normalize=normalize,
parallel=parallel,
**parallel_args,
)
@property
def gradient_approximators(self) -> list[str]:
"""The gradient approximators."""
return self.class_names