# 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.
"""Plotting a 2D Pareto front."""
from __future__ import annotations
from typing import TYPE_CHECKING
from numpy import array
if TYPE_CHECKING:
from collections.abc import Sequence
from matplotlib.axes import Axes
from numpy import ndarray
from gemseo.typing import BooleanArray
[docs]
class ParetoPlotBiObjective:
"""Plot a 2D Pareto front on a Matplotlib axes."""
def __init__(
self,
ax: Axes,
obj_values: ndarray,
pareto_optimal_loc: BooleanArray,
obj_names: Sequence[str],
all_pareto_optimal: BooleanArray,
is_non_feasible: BooleanArray,
bi_obj: bool = False,
show_non_feasible: bool = True,
) -> None:
"""
Args:
ax: The :class:`~matplotlib.axes.Axes` on which to plot.
obj_values: The objective function array, of size (n_samples, n_objs).
pareto_optimal_loc: A vector of booleans of size n_samples,
True if the point is Pareto optimal.
obj_names: The names of the objectives.
all_pareto_optimal: The indices of points that are Pareto optimal
w.r.t. all criteria.
is_non_feasible: An Array of booleans of size n_samples,
True if non_feasible.
bi_obj: True if there are only two objective values.
show_non_feasible: True to show the non-feasible points.
""" # noqa: D205, D212, D415
self.__nb_pareto_pts = obj_values.shape[0]
self.__ax = ax
self.__obj_values = obj_values
self.__pareto_optimal_loc = pareto_optimal_loc
self.__obj_names = obj_names
self.__pareto_optimal_all = all_pareto_optimal
self.__is_non_feasible = is_non_feasible
self.__bi_objective = bi_obj
self.__show_non_feasible = show_non_feasible
self.__pareto_dominated_indexes = None
# Compute the Pareto dominated indexes
self.__compute_pareto_dominated_indexes()
[docs]
def plot_on_axes(self) -> None:
"""Plot the Pareto points on the Matplolib axes."""
self.__plot_pareto_dominated_points()
self.__plot_non_feasible_points()
self.__plot_globally_pareto_optimal_points()
self.__plot_locally_pareto_optimal_points()
self.__ax.set_xlabel(self.__obj_names[0])
self.__ax.set_ylabel(self.__obj_names[1])
def __compute_pareto_dominated_indexes(self) -> None:
"""Compute the Pareto dominated indexes.
The Pareto-dominated points are all the points which are feasible, but not
locally and globally pareto optimal.
"""
pareto_dominated_indexes = array([True] * self.__nb_pareto_pts)
self.__pareto_dominated_indexes = (
pareto_dominated_indexes
& ~array(self.__is_non_feasible)
& ~self.__pareto_optimal_loc
& ~self.__pareto_optimal_all
)
def __plot_pareto_dominated_points(self) -> None:
"""Plot the Pareto-dominated points on the scatter plot."""
if True in self.__pareto_dominated_indexes:
self.__ax.scatter(
self.__obj_values[self.__pareto_dominated_indexes, 0],
self.__obj_values[self.__pareto_dominated_indexes, 1],
color="b",
label="Pareto dominated",
)
def __plot_non_feasible_points(self) -> None:
"""Plot the non-feasible points on the scatter plot."""
if True in self.__is_non_feasible and self.__show_non_feasible:
self.__ax.scatter(
self.__obj_values[self.__is_non_feasible, 0],
self.__obj_values[self.__is_non_feasible, 1],
color="r",
marker="X",
label="Non feasible point",
)
def __plot_globally_pareto_optimal_points(self) -> None:
"""Plot the globally optimal Pareto points on the scatter plot."""
if True in self.__pareto_optimal_all:
if self.__bi_objective:
label = "Pareto optimal"
else:
label = "Globally Pareto optimal"
self.__ax.scatter(
self.__obj_values[self.__pareto_optimal_all, 0],
self.__obj_values[self.__pareto_optimal_all, 1],
color="g",
label=label,
)
def __plot_locally_pareto_optimal_points(self) -> None:
"""Plot the locally optimal Pareto points on the scatter plot."""
if True in self.__pareto_optimal_loc and not self.__bi_objective:
self.__ax.scatter(
self.__obj_values[self.__pareto_optimal_loc, 0],
self.__obj_values[self.__pareto_optimal_loc, 1],
color="r",
label="Locally Pareto optimal",
)
