# Copyright 2021 IRT Saint ExupĂ©ry, https://www.irt-saintexupery.com
#
# This work is licensed under a BSD 0-Clause License.
#
# Permission to use, copy, modify, and/or distribute this software
# for any purpose with or without fee is hereby granted.
#
# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL
# WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL
# THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT,
# OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING
# FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT,
# NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION
# WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
# Contributors:
# INITIAL AUTHORS - initial API and implementation and/or initial
# documentation
# :author: Matthias De Lozzo
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""
Optimization History View
=========================
In this example, we illustrate the use of the :class:`.OptHistoryView` plot
on the Sobieski's SSBJ problem.
"""
from __future__ import annotations
from gemseo import configure_logger
from gemseo import create_discipline
from gemseo import create_scenario
from gemseo.problems.sobieski.core.design_space import SobieskiDesignSpace
# %%
# Import
# ------
# The first step is to import some high-level functions
# and a method to get the design space.
configure_logger()
# %%
# Description
# -----------
# The **OptHistoryView** post-processing
# creates a series of plots:
#
# - The design variables history - This graph shows the normalized values of the
# design variables, the :math:`y` axis is the index of the inputs in the vector;
# and the :math:`x` axis represents the iterations.
# - The objective function history - It shows the evolution of the objective
# value during the optimization.
# - The distance to the best design variables - Plots the vector
# :math:`log( ||x-x^*|| )` in log scale.
# - The history of the Hessian approximation of the objective - Plots an approximation
# of the second order derivatives of the objective function
# :math:`\frac{\partial^2 f(x)}{\partial x^2}`, which is a measure of
# the sensitivity of the function with respect to the design variables,
# and of the anisotropy of the problem (differences of curvatures in the
# design space).
# - The inequality constraint history - Portrays the evolution of the values of the
# :term:`constraints`. The inequality constraints must be non-positive, that is why
# the plot must be green or white for satisfied constraints (white = active,
# red = violated). For an :ref:`IDF formulation `, an additional
# plot is created to track the equality constraint history.
# %%
# Create disciplines
# ------------------
# At this point we instantiate the disciplines of Sobieski's SSBJ problem:
# Propulsion, Aerodynamics, Structure and Mission
disciplines = create_discipline(
[
"SobieskiPropulsion",
"SobieskiAerodynamics",
"SobieskiStructure",
"SobieskiMission",
]
)
# %%
# Create design space
# -------------------
# We also create the :class:`.SobieskiDesignSpace`.
design_space = SobieskiDesignSpace()
# %%
# Create and execute scenario
# ---------------------------
# The next step is to build an MDO scenario in order to maximize the range,
# encoded 'y_4', with respect to the design parameters, while satisfying the
# inequality constraints 'g_1', 'g_2' and 'g_3'. We can use the MDF formulation,
# the SLSQP optimization algorithm
# and a maximum number of iterations equal to 100.
scenario = create_scenario(
disciplines,
formulation="MDF",
objective_name="y_4",
maximize_objective=True,
design_space=design_space,
)
scenario.set_differentiation_method()
for constraint in ["g_1", "g_2", "g_3"]:
scenario.add_constraint(constraint, "ineq")
scenario.execute({"algo": "SLSQP", "max_iter": 100})
# %%
# Post-process scenario
# ---------------------
# Lastly, we post-process the scenario by means of the :class:`.OptHistoryView`
# plot which plots the history of optimization for both objective function,
# constraints, design parameters and distance to the optimum.
# %%
# .. tip::
#
# Each post-processing method requires different inputs and offers a variety
# of customization options. Use the high-level function
# :func:`.get_post_processing_options_schema` to print a table with
# the options for any post-processing algorithm.
# Or refer to our dedicated page:
# :ref:`gen_post_algos`.
scenario.post_process(
"OptHistoryView", save=False, show=True, variable_names=["x_2", "x_1"]
)