Source code for gemseo.post.gradient_sensitivity

# 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
#        :author: Damien Guenot
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""Plot the derivatives of the functions."""

from __future__ import annotations

import logging
from typing import TYPE_CHECKING

from matplotlib import pyplot
from numpy import arange
from numpy import atleast_2d
from numpy import ndarray
from numpy import where

from gemseo.post.opt_post_processor import OptPostProcessor
from gemseo.utils.string_tools import repr_variable

if TYPE_CHECKING:
    from collections.abc import Iterable
    from collections.abc import Mapping

    from matplotlib.figure import Figure

LOGGER = logging.getLogger(__name__)


[docs] class GradientSensitivity(OptPostProcessor): """Derivatives of the objective and constraints at a given iteration.""" DEFAULT_FIG_SIZE = (10.0, 10.0) def _plot( self, iteration: int | None = None, scale_gradients: bool = False, compute_missing_gradients: bool = False, ) -> None: """ Args: iteration: The iteration to plot the sensitivities. Can use either positive or negative indexing, e.g. ``5`` for the 5-th iteration or ``-2`` for the penultimate one. If ``None``, use the iteration of the optimum. scale_gradients: If ``True``, normalize each gradient w.r.t. the design variables. compute_missing_gradients: Whether to compute the gradients at the selected iteration if they were not computed by the algorithm. .. warning:: Activating this option may add considerable computation time depending on the cost of the gradient evaluation. This option will not compute the gradients if the :class:`.OptimizationProblem` instance was imported from an HDF5 file. This option requires an :class:`.OptimizationProblem` with a gradient-based algorithm. """ # noqa: D205, D212, D415 if iteration is None: design_value = self.optimization_problem.solution.x_opt else: design_value = self.optimization_problem.database.get_x_vect(iteration) fig = self.__generate_subplots( self._get_design_variable_names(), design_value, self.__get_output_gradients( design_value, scale_gradients=scale_gradients, compute_missing_gradients=compute_missing_gradients, ), scale_gradients=scale_gradients, ) self._add_figure(fig) def __get_output_gradients( self, design_value: ndarray, scale_gradients: bool = False, compute_missing_gradients: bool = False, ) -> dict[str, ndarray]: """Return the gradients of all the output variable at a given design value. Args: design_value: The value of the design vector. scale_gradients: Whether to normalize the gradients w.r.t. the design variables. compute_missing_gradients: Whether to compute the gradients at the selected iteration if they were not computed by the algorithm. .. warning:: Activating this option may add considerable computation time depending on the cost of the gradient evaluation. This option will not compute the gradients if the :class:`.OptimizationProblem` instance was imported from an HDF5 file. This option requires an :class:`.OptimizationProblem` with a gradient-based algorithm. Returns: The gradients of the outputs indexed by the names of the output, e.g. ``"output_name"`` for a mono-dimensional output, or ``"output_name_i"`` for the i-th component of a multidimensional output. """ gradient_values = {} if compute_missing_gradients: try: _, gradient_values = self.optimization_problem.evaluate_functions( design_value, no_db_no_norm=True, eval_jac=True, eval_observables=False, normalize=False, ) except NotImplementedError: LOGGER.info( "The missing gradients for an OptimizationProblem without " "callable functions cannot be computed." ) function_names = self.optimization_problem.get_all_function_name() scale_gradient = self.optimization_problem.design_space.unnormalize_vect function_names_to_gradients = {} for function_name in function_names: if compute_missing_gradients and gradient_values: gradient_value = gradient_values[function_name] else: gradient_value = self.database.get_function_value( self.database.get_gradient_name(function_name), design_value ) if gradient_value is None: continue gradient_value = atleast_2d(gradient_value) size = len(gradient_value) for i, _gradient_value in enumerate(gradient_value): if scale_gradients: _gradient_value = scale_gradient(_gradient_value, minus_lb=False) function_names_to_gradients[repr_variable(function_name, i, size)] = ( _gradient_value ) return function_names_to_gradients def __generate_subplots( self, design_names: Iterable[str], design_value: ndarray, gradients: Mapping[str, ndarray], scale_gradients: bool = False, ) -> Figure: """Generate the gradients subplots from the data. Args: design_names: The names of the design variables. design_value: The reference value for x. gradients: The gradients to plot indexed by the output names. scale_gradients: Whether to normalize the gradients w.r.t. the design variables. Returns: The gradients subplots. Raises: ValueError: If `gradients` is empty. """ n_gradients = len(gradients) if n_gradients == 0: msg = "No gradients to plot at current iteration." raise ValueError(msg) n_cols = 2 n_rows = sum(divmod(n_gradients, n_cols)) fig, axes = pyplot.subplots( nrows=n_rows, ncols=n_cols, sharex=True, figsize=self.DEFAULT_FIG_SIZE ) axes = atleast_2d(axes) abscissa = arange(len(design_value)) if self._change_obj: gradients[self._obj_name] = -gradients.pop(self._standardized_obj_name) i = j = 0 font_size = 12 rotation = 90 for output_name, gradient_value in sorted(gradients.items()): axe = axes[i][j] axe.bar( abscissa, gradient_value, color=where(gradient_value < 0, "blue", "red"), align="center", ) axe.grid() axe.set_axisbelow(True) axe.set_title(output_name) axe.set_xticks(abscissa) axe.set_xticklabels(design_names, fontsize=font_size, rotation=rotation) # Update y labels spacing vis_labels = [ label for label in axe.get_yticklabels() if label.get_visible() is True ] pyplot.setp(vis_labels[::2], visible=False) if j == n_cols - 1: j = 0 i += 1 else: j += 1 if j == n_cols - 1: axe = axes[i][j] axe.set_xticks(abscissa) axe.set_xticklabels(design_names, fontsize=font_size, rotation=rotation) title = ( "Derivatives of objective and constraints with respect to design variables" ) if scale_gradients: fig.suptitle(f"{title}\n\nNormalized design space.", fontsize=14) else: fig.suptitle(title, fontsize=14) return fig