# 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 - initial API and implementation and/or initial
# documentation
# :author: Damien Guenot
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""PyDOE algorithms wrapper."""
from __future__ import annotations
from collections.abc import Callable
from collections.abc import Sequence
from typing import TYPE_CHECKING
from typing import ClassVar
from typing import Final
from typing import Optional
from typing import Union
from numpy.random import RandomState
from pyDOE3.doe_box_behnken import bbdesign
from pyDOE3.doe_composite import ccdesign
from pyDOE3.doe_factorial import ff2n
from pyDOE3.doe_lhs import lhs
from pyDOE3.doe_plackett_burman import pbdesign
from gemseo.algos._unsuitability_reason import _UnsuitabilityReason
from gemseo.algos.doe.base_doe_library import BaseDOELibrary
from gemseo.algos.doe.base_doe_library import DOEAlgorithmDescription
from gemseo.algos.doe.pydoe.pydoe_full_factorial_doe import PyDOEFullFactorialDOE
from gemseo.algos.doe.pydoe.settings.pydoe_bbdesign import PYDOE_BBDESIGN_Settings
from gemseo.algos.doe.pydoe.settings.pydoe_ccdesign import PYDOE_CCDESIGN_Settings
from gemseo.algos.doe.pydoe.settings.pydoe_ff2n import PYDOE_FF2N_Settings
from gemseo.algos.doe.pydoe.settings.pydoe_fullfact import PYDOE_FULLFACT_Settings
from gemseo.algos.doe.pydoe.settings.pydoe_lhs import PYDOE_LHS_Settings
from gemseo.algos.doe.pydoe.settings.pydoe_pbdesign import PYDOE_PBDESIGN_Settings
from gemseo.typing import RealArray
if TYPE_CHECKING:
from gemseo.algos.design_space import DesignSpace
from gemseo.algos.optimization_problem import OptimizationProblem
OptionType = Optional[
Union[str, int, float, bool, Sequence[int], tuple[int, int], RealArray, RandomState]
]
[docs]
class PyDOEAlgorithmDescription(DOEAlgorithmDescription):
"""The description of a pyDOE algorithm."""
library_name = "PyDOE"
"""The library name."""
[docs]
class PyDOELibrary(BaseDOELibrary):
"""The PyDOE DOE algorithms library."""
__NAMES_TO_FUNCTIONS: ClassVar[dict[str, Callable]] = {
"PYDOE_BBDESIGN": bbdesign,
"PYDOE_CCDESIGN": ccdesign,
"PYDOE_FF2N": ff2n,
"PYDOE_LHS": lhs,
"PYDOE_PBDESIGN": pbdesign,
}
"""The algorithm names bound to the corresponding pyDOE function."""
__DOC: Final[str] = "https://pythonhosted.org/pyDOE/"
ALGORITHM_INFOS: ClassVar[dict[str, DOEAlgorithmDescription]] = {
"PYDOE_BBDESIGN": PyDOEAlgorithmDescription(
algorithm_name="PYDOE_BBDESIGN",
description="Box-Behnken design",
internal_algorithm_name="bbdesign",
website=f"{__DOC}rsm.html#box-behnken",
Settings=PYDOE_BBDESIGN_Settings,
minimum_dimension=3,
),
"PYDOE_CCDESIGN": PyDOEAlgorithmDescription(
algorithm_name="PYDOE_CCDESIGN",
description="Central Composite",
internal_algorithm_name="ccdesign",
website=f"{__DOC}rsm.html#central-composite",
Settings=PYDOE_CCDESIGN_Settings,
minimum_dimension=2,
),
"PYDOE_FF2N": PyDOEAlgorithmDescription(
algorithm_name="PYDOE_FF2N",
description="2-Level Full-Factorial",
internal_algorithm_name="ff2n",
website=f"{__DOC}factorial.html#level-full-factorial",
Settings=PYDOE_FF2N_Settings,
),
"PYDOE_FULLFACT": PyDOEAlgorithmDescription(
algorithm_name="PYDOE_FULLFACT",
description="Full-Factorial",
internal_algorithm_name="fullfact",
website=f"{__DOC}factorial.html#general-full-factorial",
Settings=PYDOE_FULLFACT_Settings,
),
"PYDOE_LHS": PyDOEAlgorithmDescription(
algorithm_name="PYDOE_LHS",
description="Latin Hypercube Sampling",
internal_algorithm_name="lhs",
website=f"{__DOC}randomized.html#latin-hypercube",
Settings=PYDOE_LHS_Settings,
),
"PYDOE_PBDESIGN": PyDOEAlgorithmDescription(
algorithm_name="PYDOE_PBDESIGN",
description="Plackett-Burman design",
internal_algorithm_name="pbdesign",
website=f"{__DOC}factorial.html#plackett-burman",
Settings=PYDOE_PBDESIGN_Settings,
),
}
def _generate_unit_samples(
self, design_space: DesignSpace, **settings: OptionType
) -> RealArray:
n = design_space.dimension
if self._algo_name == "PYDOE_FULLFACT":
return PyDOEFullFactorialDOE().generate_samples(dimension=n, **settings)
doe_algorithm = self.__NAMES_TO_FUNCTIONS[self._algo_name]
if self._algo_name == "PYDOE_LHS":
settings["random_state"] = RandomState(
self._seeder.get_seed(settings["random_state"])
)
settings["samples"] = settings["n_samples"]
del settings["n_samples"]
return doe_algorithm(n, **settings)
return self.__scale(doe_algorithm(n, **settings))
@staticmethod
def __scale(result: RealArray) -> RealArray:
"""Scale the DOE design variables to [0, 1].
Args:
result: The design variables to be scaled.
Returns:
The scaled design variables.
"""
return (result + 1.0) * 0.5
@classmethod
def _get_unsuitability_reason(
cls,
algorithm_description: DOEAlgorithmDescription,
problem: OptimizationProblem,
) -> _UnsuitabilityReason:
reason = super()._get_unsuitability_reason(algorithm_description, problem)
if (
reason
or problem.design_space.dimension >= algorithm_description.minimum_dimension
):
return reason
return _UnsuitabilityReason.SMALL_DIMENSION
