# 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
"""Finite differences approximation."""
from __future__ import annotations
import logging
import pickle
from collections.abc import Iterable
from collections.abc import Mapping
from collections.abc import Sequence
from collections.abc import Sized
from contextlib import contextmanager
from multiprocessing import cpu_count
from pathlib import Path
from typing import TYPE_CHECKING
from scipy.sparse import hstack as sparse_hstack
from gemseo.utils.compatibility.scipy import sparse_classes
from gemseo.utils.data_conversion import split_array_to_dict_of_arrays
from gemseo.utils.derivatives.approximation_modes import ApproximationMode
from gemseo.utils.derivatives.error_estimators import EPSILON
from gemseo.utils.derivatives.gradient_approximator_factory import (
GradientApproximatorFactory,
)
from gemseo.utils.matplotlib_figure import save_show_figure
if TYPE_CHECKING:
from numbers import Number
from matplotlib.pyplot import Figure
from gemseo.core.discipline import MDODiscipline
from gemseo.core.discipline_data import DisciplineData
from gemseo.utils.derivatives.gradient_approximator import GradientApproximator
from matplotlib import pyplot as plt
from numpy import absolute
from numpy import allclose
from numpy import amax
from numpy import arange
from numpy import atleast_2d
from numpy import concatenate
from numpy import divide
from numpy import ndarray
from numpy import zeros
LOGGER = logging.getLogger(__name__)
[docs]
class DisciplineJacApprox:
"""Approximates a discipline Jacobian using finite differences or Complex step."""
N_CPUS = cpu_count()
approximator: GradientApproximator | None
"""The gradient approximation method."""
def __init__(
self,
discipline: MDODiscipline,
approx_method: ApproximationMode = ApproximationMode.FINITE_DIFFERENCES,
step: Number | Iterable[Number] = 1e-7,
parallel: bool = False,
n_processes: int = N_CPUS,
use_threading: bool = False,
wait_time_between_fork: float = 0,
) -> None:
"""
Args:
discipline: The discipline
for which the Jacobian approximation shall be made.
approx_method: The approximation method,
either ``complex_step`` or ``finite_differences``.
step: The differentiation step. The ``finite_differences`` takes either
a float or an iterable of floats with the same length as the inputs.
The ``complex_step`` method takes either a complex or a float as input.
parallel: Whether to differentiate the discipline in parallel.
n_processes: The maximum simultaneous number of threads,
if ``use_threading`` is True, or processes otherwise,
used to parallelize the execution.
use_threading: Whether to use threads instead of processes
to parallelize the execution;
multiprocessing will copy (serialize) all the disciplines,
while threading will share all the memory
This is important to note
if you want to execute the same discipline multiple times,
you shall use multiprocessing.
wait_time_between_fork: The time waited between two forks
of the process / thread.
""" # noqa:D205 D212 D415
from gemseo.core.mdofunctions.mdo_discipline_adapter_generator import (
MDODisciplineAdapterGenerator,
)
self.discipline = discipline
self.approx_method = approx_method
self.step = step
self.generator = MDODisciplineAdapterGenerator(discipline)
self.func = None
self.approximator = None
self.auto_steps = {}
self.__par_args = {
"n_processes": n_processes,
"use_threading": use_threading,
"wait_time_between_fork": wait_time_between_fork,
}
self.__parallel = parallel
def _create_approximator(
self,
outputs: Sequence[str],
inputs: Sequence[str],
) -> None:
"""Create the Jacobian approximation class.
Args:
inputs: The names of the inputs used to differentiate the outputs.
outputs: The names of the outputs to be differentiated.
Raises:
ValueError: If the Jacobian approximation method is unknown.
"""
self.func = self.generator.get_function(
input_names=inputs, output_names=outputs
)
self.approximator = GradientApproximatorFactory().create(
self.approx_method,
self.func,
step=self.step,
parallel=self.__parallel,
**self.__par_args,
)
[docs]
def auto_set_step(
self,
outputs: Sequence[str],
inputs: Sequence[str],
print_errors: bool = True,
numerical_error: float = EPSILON,
) -> tuple[ndarray, dict[str, ndarray]]:
r"""Compute the optimal step.
Require a first evaluation of the perturbed functions values.
The optimal step is reached when the truncation error
(cut in the Taylor development),
and the numerical cancellation errors
(round-off when doing :math:`f(x+step)-f(x))` are equal.
Args:
inputs: The names of the inputs used to differentiate the outputs.
outputs: The names of the outputs to be differentiated.
print_errors: Whether to log the cancellation
and truncation error estimates.
numerical_error: The numerical error
associated to the calculation of :math:`f`.
By default, Machine epsilon (appx 1e-16),
but can be higher.
when the calculation of :math:`f` requires a numerical resolution.
See Also:
https://en.wikipedia.org/wiki/Numerical_differentiation
and *Numerical Algorithms and Digital Representation*,
Knut Morken, Chapter 11, "Numerical Differentiation"
Returns:
The Jacobian of the function.
"""
self._create_approximator(outputs, inputs)
with self.__set_zero_cache_tol():
compute_opt_step = self.approximator.compute_optimal_step
x_vect = self._prepare_xvect(inputs, self.discipline.default_inputs)
steps_opt, errors = compute_opt_step(x_vect, numerical_error=numerical_error)
if print_errors:
LOGGER.info(
"Set optimal step for finite differences. "
"Estimated approximation errors ="
)
LOGGER.info(errors)
data = self.discipline.default_inputs or self.discipline.local_data
names_to_slices = (
self.discipline.input_grammar.data_converter.compute_names_to_slices(
inputs,
data,
)[0]
)
self.auto_steps = (
self.discipline.input_grammar.data_converter.convert_array_to_data(
steps_opt, names_to_slices
)
)
return errors, self.auto_steps
@contextmanager
def __set_zero_cache_tol(self) -> None:
"""A context manager to temporary set the discipline cache tolerance to zero."""
old_cache_tol = self.discipline.cache_tol
self.discipline.cache_tol = 0.0
yield
self.discipline.cache_tol = old_cache_tol
def _prepare_xvect(
self,
inputs: Iterable[str],
data: DisciplineData | None = None,
) -> ndarray:
"""Convert an input data mapping into an input array.
Args:
inputs: The names of the inputs to be used for the differentiation.
data: The input data mapping.
If ``None``, use the local data of the discipline.
Returns:
The input array.
"""
if data is None:
data = self.discipline.local_data
return self.discipline.input_grammar.data_converter.convert_data_to_array(
inputs,
data,
)
[docs]
def compute_approx_jac(
self,
outputs: Iterable[str],
inputs: Iterable[str],
x_indices: Sequence[int] | None = None,
) -> dict[str, dict[str, ndarray]]:
"""Approximate the Jacobian.
Args:
outputs: The names of the outputs to be differentiated.
inputs: The names of the inputs used to differentiate the outputs.
x_indices: The components of the input vector
to be used for the differentiation.
If ``None``, use all the components.
Returns:
The approximated Jacobian.
"""
self._create_approximator(outputs, inputs)
if self.auto_steps and all(key in self.auto_steps for key in inputs):
step = self.discipline.input_grammar.data_converter.convert_data_to_array(
inputs,
self.auto_steps,
)
else:
step = self.step
x_vect = self._prepare_xvect(inputs, self.discipline.local_data)
if isinstance(step, Sized) and 1 < len(step) != len(x_vect):
msg = f"Inconsistent step size, expected {x_vect.size} got {len(step)}."
raise ValueError(msg)
with self.__set_zero_cache_tol():
flat_jac = atleast_2d(
self.approximator.f_gradient(x_vect, x_indices=x_indices, step=step)
)
data_names_to_sizes = (
self.discipline.output_grammar.data_converter.compute_names_to_sizes(
outputs,
self.discipline.local_data,
)
)
input_names_to_sizes = (
self.discipline.input_grammar.data_converter.compute_names_to_sizes(
inputs,
self.discipline.local_data,
)
)
if x_indices is None:
flat_jac_complete = flat_jac
else:
flat_jac_complete = zeros([
sum(data_names_to_sizes.values()),
sum(input_names_to_sizes.values()),
])
flat_jac_complete[:, x_indices] = flat_jac
data_names_to_sizes.update(input_names_to_sizes)
return split_array_to_dict_of_arrays(
flat_jac_complete, data_names_to_sizes, outputs, inputs
)
[docs]
def check_jacobian(
self,
analytic_jacobian: dict[str, dict[str, ndarray]],
outputs: Iterable[str],
inputs: Iterable[str],
discipline: MDODiscipline,
threshold: float = 1e-8,
plot_result: bool = False,
file_path: str | Path = "jacobian_errors.pdf",
show: bool = False,
fig_size_x: float = 10.0,
fig_size_y: float = 10.0,
reference_jacobian_path: str | Path | None = None,
save_reference_jacobian: bool = False,
indices: int | Sequence[int] | slice | Ellipsis | None = None,
) -> bool:
"""Check if the analytical Jacobian is correct with respect to a reference one.
If `reference_jacobian_path` is not `None`
and `save_reference_jacobian` is `True`,
compute the reference Jacobian with the approximation method
and save it in `reference_jacobian_path`.
If `reference_jacobian_path` is not `None`
and `save_reference_jacobian` is `False`,
do not compute the reference Jacobian
but read it from `reference_jacobian_path`.
If `reference_jacobian_path` is `None`,
compute the reference Jacobian without saving it.
Args:
analytic_jacobian: The Jacobian to validate.
inputs: The names of the inputs used to differentiate the outputs.
outputs: The names of the outputs to be differentiated.
discipline: The discipline to be differentiated.
threshold: The acceptance threshold for the Jacobian error.
plot_result: Whether to plot the result of the validation
(computed vs approximated Jacobians).
file_path: The path to the output file if ``plot_result`` is ``True``.
show: Whether to open the figure.
fig_size_x: The x-size of the figure in inches.
fig_size_y: The y-size of the figure in inches.
reference_jacobian_path: The path of the reference Jacobian file.
save_reference_jacobian: Whether to save the reference Jacobian.
indices: The indices of the inputs and outputs
for the different sub-Jacobian matrices,
formatted as ``{variable_name: variable_components}``
where ``variable_components`` can be either
an integer, e.g. `2`
a sequence of integers, e.g. `[0, 3]`,
a slice, e.g. `slice(0,3)`,
the ellipsis symbol (`...`)
or `None`, which is the same as ellipsis.
If a variable name is missing, consider all its components.
If ``None``,
consider all the components of all the ``inputs`` and ``outputs``.
Returns:
Whether the analytical Jacobian is correct.
"""
input_names_to_indices = None
input_indices = None
if indices is not None:
input_indices, input_names_to_indices = self._compute_variable_indices(
indices,
inputs,
self.discipline.input_grammar.data_converter.compute_names_to_sizes(
inputs,
self.discipline.default_inputs,
),
)
if reference_jacobian_path is None or save_reference_jacobian:
approximated_jacobian = self.compute_approx_jac(
outputs, inputs, input_indices
)
else:
with Path(reference_jacobian_path).open("rb") as infile:
approximated_jacobian = pickle.load(infile)
if save_reference_jacobian:
with Path(reference_jacobian_path).open("wb") as outfile:
pickle.dump(approximated_jacobian, outfile)
output_names_to_indices = None
if indices is not None:
output_sizes = {
output_name: output_jacobian[next(iter(output_jacobian))].shape[0]
for output_name, output_jacobian in approximated_jacobian.items()
}
_, output_names_to_indices = self._compute_variable_indices(
indices, outputs, output_sizes
)
if input_names_to_indices is None:
input_names_to_indices = Ellipsis
if output_names_to_indices is None:
output_names_to_indices = Ellipsis
succeed = True
for output_name, output_jacobian in approximated_jacobian.items():
for input_name, approx_jac in output_jacobian.items():
computed_jac = analytic_jacobian[output_name][input_name]
if indices is not None:
row_idx = atleast_2d(output_names_to_indices[output_name]).T
col_idx = input_names_to_indices[input_name]
computed_jac = computed_jac[row_idx, col_idx]
approx_jac = approx_jac[row_idx, col_idx]
if approx_jac.shape != computed_jac.shape:
succeed = False
msg = (
f"{discipline.name} Jacobian: dp {output_name}/dp {input_name} "
"is of wrong shape; "
f"got: {computed_jac.shape} while expected: {approx_jac.shape}."
)
LOGGER.error(msg)
else:
if isinstance(computed_jac, sparse_classes):
computed_jac = computed_jac.toarray()
success_loc = allclose(
computed_jac, approx_jac, atol=threshold, rtol=threshold
)
if not success_loc:
err = amax(
divide(
absolute(computed_jac - approx_jac),
absolute(approx_jac) + 1.0,
)
)
LOGGER.error(
"%s Jacobian: dp %s/d %s is wrong by %s%%.",
discipline.name,
output_name,
input_name,
err * 100.0,
)
LOGGER.info("Approximate jacobian = \n%s", approx_jac)
LOGGER.info("Provided by linearize method = \n%s", computed_jac)
LOGGER.info(
"Difference of jacobians = \n%s", approx_jac - computed_jac
)
succeed = succeed and success_loc
else:
LOGGER.info(
"Jacobian: dp %s/dp %s succeeded.", output_name, input_name
)
LOGGER.info(
"Linearization of MDODiscipline: %s is %s.",
discipline.name,
"correct" if succeed else "wrong",
)
if plot_result:
self.plot_jac_errors(
analytic_jacobian,
approximated_jacobian,
file_path,
show,
fig_size_x,
fig_size_y,
)
return succeed
@staticmethod
def _compute_variable_indices(
indices: Mapping[str, int | Sequence[int] | Ellipsis | slice],
variable_names: Iterable[str],
variable_sizes: Mapping[str, int],
) -> tuple[list[int], dict[str, int]]:
"""Return indices.
Args:
indices: The indices for variables
formatted as ``{variable_name: variable_components}``
where ``variable_components`` can be either
an integer, e.g. `2`
a sequence of integers, e.g. `[0, 3]`,
a slice, e.g. `slice(0,3)`,
the ellipsis symbol (`...`)
or `None`, which is the same as ellipsis.
If a variable name is missing, consider all its components.
variable_names: The names of the variables.
Returns:
The indices of the variables.
"""
indices_sequence = []
names_to_indices = {}
variable_position = 0
for variable_name in variable_names:
variable_size = variable_sizes[variable_name]
variable_indices = list(range(variable_size))
indices_sequence.append(indices.get(variable_name, variable_indices))
if isinstance(indices_sequence[-1], int):
indices_sequence[-1] = [indices_sequence[-1]]
if isinstance(indices_sequence[-1], slice):
indices_sequence[-1] = variable_indices[indices_sequence[-1]]
if indices_sequence[-1] in [Ellipsis, None]:
indices_sequence[-1] = variable_indices
names_to_indices[variable_name] = indices_sequence[-1]
indices_sequence[-1] = [
variable_index + variable_position
for variable_index in indices_sequence[-1]
]
variable_position += variable_size
indices_sequence = [item for sublist in indices_sequence for item in sublist]
return indices_sequence, names_to_indices
@staticmethod
def __concatenate_jacobian_per_output_names(
analytic_jacobian: dict[str, dict[str, ndarray]],
approximated_jacobian: dict[str, dict[str, ndarray]],
) -> tuple[dict[str, ndarray], dict[str, ndarray], list[str]]:
"""Concatenate the Jacobian matrices per output name.
Args:
analytic_jacobian: The reference Jacobian
of the form ``{output_name: {input_name: sub_jacobian}}``.
approximated_jacobian: The approximated Jacobian
of the form ``{output_name: {input_name: sub_jacobian}}``.
Returns:
The analytic Jacobian of the form ``{output_name: sub_jacobian}``,
the approximated Jacobian of the form ``{output_name: sub_jacobian}``
and the names of the output components
corresponding to the columns of ``sub_jacobian``.
"""
_approx_jacobian = {}
_analytic_jacobian = {}
jacobian = analytic_jacobian[next(iter(analytic_jacobian))]
input_names = list(jacobian.keys())
input_component_names = [
f"{input_name}_{i + 1}"
for input_name in input_names
for i in range(jacobian[input_name].shape[1])
]
for output_name, output_approximated_jacobian in approximated_jacobian.items():
_output_approx_jacobian = concatenate(
[
output_approximated_jacobian[input_name]
for input_name in input_names
],
axis=1,
)
analytic_jacobian_out = analytic_jacobian[output_name]
contains_sparse = any(
isinstance(analytic_jacobian_out[input_name], sparse_classes)
for input_name in input_names
)
if contains_sparse:
_output_analytic_jacobian = sparse_hstack(
[analytic_jacobian_out[input_name] for input_name in input_names],
).tocsr()
else:
_output_analytic_jacobian = concatenate(
[analytic_jacobian_out[input_name] for input_name in input_names],
axis=1,
)
n_f = len(_output_approx_jacobian)
if n_f == 1:
_approx_jacobian[output_name] = _output_approx_jacobian.flatten()
if contains_sparse:
_output_analytic_jacobian = _output_analytic_jacobian.toarray()
_analytic_jacobian[output_name] = _output_analytic_jacobian.flatten()
else:
for i in range(n_f):
output_name = f"{output_name}_{i}"
_approx_jacobian[output_name] = _output_approx_jacobian[i, :]
_analytic_jacobian[output_name] = _output_analytic_jacobian[i, :]
return _analytic_jacobian, _approx_jacobian, input_component_names
[docs]
def plot_jac_errors(
self,
computed_jac: ndarray,
approx_jac: ndarray,
file_path: str | Path = "jacobian_errors.pdf",
show: bool = False,
fig_size_x: float = 10.0,
fig_size_y: float = 10.0,
) -> Figure:
"""Generate a plot of the exact vs approximated Jacobian.
Args:
computed_jac: The Jacobian to validate.
approx_jac: The approximated Jacobian.
file_path: The path to the output file if ``plot_result`` is ``True``.
show: Whether to open the figure.
fig_size_x: The x-size of the figure in inches.
fig_size_y: The y-size of the figure in inches.
"""
comp_grad, app_grad, x_labels = self.__concatenate_jacobian_per_output_names(
computed_jac, approx_jac
)
n_funcs = len(app_grad)
if n_funcs == 0:
msg = "No gradients to plot!"
raise ValueError(msg)
nrows = n_funcs // 2
if 2 * nrows < n_funcs:
nrows += 1
ncols = 2
fig, axes = plt.subplots(
nrows=nrows, ncols=2, sharex=True, figsize=(fig_size_x, fig_size_y)
)
i = 0
j = -1
axes = atleast_2d(axes)
n_subplots = len(axes) * len(axes[0])
abscissa = arange(len(x_labels))
for func, grad in sorted(comp_grad.items()):
if isinstance(grad, sparse_classes):
grad = grad.toarray().flatten()
j += 1
if j == ncols:
j = 0
i += 1
axe = axes[i][j]
axe.plot(abscissa, grad, "bo")
axe.plot(abscissa, app_grad[func], "ro")
axe.set_title(func)
axe.set_xticklabels(x_labels, fontsize=14)
axe.set_xticks(abscissa)
for tick in axe.get_xticklabels():
tick.set_rotation(90)
# Update y labels spacing
vis_labels = [
label for label in axe.get_yticklabels() if label.get_visible() is True
]
plt.setp(vis_labels[::2], visible=False)
# plt.xticks(rotation=90)
if len(comp_grad.items()) < n_subplots:
# xlabel must be written with the same fontsize on the 2 columns
j += 1
axe = axes[i][j]
axe.set_xticklabels(x_labels, fontsize=14)
axe.set_xticks(abscissa)
for tick in axe.get_xticklabels():
tick.set_rotation(90)
fig.suptitle(
"Computed and approximate derivatives. "
" blue = computed, red = approximated derivatives",
fontsize=14,
)
save_show_figure(fig, show, file_path)
return fig
