# 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
# :author: Francois Gallard
"""Design of experiments from custom data."""
from __future__ import annotations
import logging
from collections.abc import Mapping
from collections.abc import Sequence
from pathlib import Path
from typing import TYPE_CHECKING
from typing import ClassVar
from typing import Final
from typing import Optional
from typing import TextIO
from typing import Union
from numpy import apply_along_axis
from numpy import ndarray
from numpy import vstack
from pandas import read_csv
from gemseo.algos.doe.base_doe_library import BaseDOELibrary
from gemseo.algos.doe.base_doe_library import DOEAlgorithmDescription
from gemseo.algos.doe.custom_doe.settings.custom_doe_settings import CustomDOE_Settings
from gemseo.typing import RealArray
if TYPE_CHECKING:
from gemseo.algos.design_space import DesignSpace
OptionType = Optional[Union[str, int, float, bool, list[str], Path, TextIO, RealArray]]
LOGGER = logging.getLogger(__name__)
[docs]
class CustomDOE(BaseDOELibrary):
"""A design of experiments from samples provided as a file or an array.
The samples are provided either as a file in text or csv format or as a sequence of
sequences of numbers, e.g. a 2D numpy array.
A csv file format is assumed to have a header whereas a text file (extension .txt)
does not.
"""
_COMMENTS_KEYWORD: Final[str] = "comments"
_DELIMITER_KEYWORD: Final[str] = "delimiter"
_DOE_FILE: Final[str] = "doe_file"
_SAMPLES: Final[str] = "samples"
_SKIPROWS_KEYWORD: Final[str] = "skiprows"
_USE_UNIT_HYPERCUBE: ClassVar[bool] = False
ALGORITHM_INFOS: ClassVar[dict[str, DOEAlgorithmDescription]] = {
"CustomDOE": DOEAlgorithmDescription(
algorithm_name="CustomDOE",
description=(
"This samples are provided "
"either as a file in text or csv format "
"or as a sequence of sequences of numbers."
),
internal_algorithm_name="CustomDOE",
library_name="CustomDOE",
Settings=CustomDOE_Settings,
)
}
def __init__(self, algo_name: str = "CustomDOE") -> None: # noqa:D107
super().__init__(algo_name)
[docs]
@staticmethod
def read_file(
doe_file: str | Path | TextIO,
delimiter: str | None = ",",
comments: str | Sequence[str] | None = "#",
skiprows: int = 0,
) -> RealArray:
"""Read a file containing several samples (one per line) and return them.
Args:
doe_file: Either the file, the filename, or the generator to read.
delimiter: The character used to separate values.
If ``None``, use whitespace.
comments: The characters or list of characters
used to indicate the start of a comment.
``None`` implies no comments.
skiprows: Skip the first ``skiprows`` lines.
Returns:
The samples.
"""
try:
samples = read_csv(
doe_file,
delimiter=delimiter,
skiprows=skiprows,
header=None,
comment=comments,
).to_numpy()
except Exception:
LOGGER.exception("Failed to load the DOE file %s", doe_file)
raise
return samples
def _generate_unit_samples(
self, design_space: DesignSpace, **settings: OptionType
) -> RealArray:
"""
Raises:
ValueError: If the dimension of ``samples`` is different from the
one of the problem.
""" # noqa: D205, D212, D415
samples = settings.get(self._SAMPLES)
doe_file = settings.get(self._DOE_FILE)
dimension = design_space.dimension
if doe_file:
samples = self.read_file(
doe_file,
comments=settings[self._COMMENTS_KEYWORD],
delimiter=settings[self._DELIMITER_KEYWORD],
skiprows=settings[self._SKIPROWS_KEYWORD],
)
if isinstance(samples, Mapping):
samples = design_space.convert_dict_to_array(samples)
elif not isinstance(samples, ndarray):
samples = vstack([
design_space.convert_dict_to_array(sample) for sample in samples
])
if samples.shape[1] != dimension:
msg = (
f"Dimension mismatch between the variables space ({dimension}) "
f"and the samples ({samples.shape[1]})."
)
raise ValueError(msg)
return apply_along_axis(design_space.transform_vect, axis=1, arr=samples)
