Note
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Constraints history¶
In this example, we illustrate the use of the ConstraintsHistory
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 functions from the API and a method to get the design space.
configure_logger()
<RootLogger root (INFO)>
Description¶
The ConstraintsHistory
post-processing
plots the constraints functions history in line charts
with violation indication by color on the background.
This plot is more precise than the constraint plot provided by the opt_history_view but scales less with the number of constraints.
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 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()
all_constraints = ["g_1", "g_2", "g_3"]
for constraint in all_constraints:
scenario.add_constraint(constraint, "ineq")
scenario.execute({"algo": "SLSQP", "max_iter": 10})
INFO - 10:55:09:
INFO - 10:55:09: *** Start MDOScenario execution ***
INFO - 10:55:09: MDOScenario
INFO - 10:55:09: Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
INFO - 10:55:09: MDO formulation: MDF
INFO - 10:55:09: Optimization problem:
INFO - 10:55:09: minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 10:55:09: with respect to x_1, x_2, x_3, x_shared
INFO - 10:55:09: subject to constraints:
INFO - 10:55:09: g_1(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 10:55:09: g_2(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 10:55:09: g_3(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 10:55:09: over the design space:
INFO - 10:55:09: +-------------+-------------+-------+-------------+-------+
INFO - 10:55:09: | Name | Lower bound | Value | Upper bound | Type |
INFO - 10:55:09: +-------------+-------------+-------+-------------+-------+
INFO - 10:55:09: | x_shared[0] | 0.01 | 0.05 | 0.09 | float |
INFO - 10:55:09: | x_shared[1] | 30000 | 45000 | 60000 | float |
INFO - 10:55:09: | x_shared[2] | 1.4 | 1.6 | 1.8 | float |
INFO - 10:55:09: | x_shared[3] | 2.5 | 5.5 | 8.5 | float |
INFO - 10:55:09: | x_shared[4] | 40 | 55 | 70 | float |
INFO - 10:55:09: | x_shared[5] | 500 | 1000 | 1500 | float |
INFO - 10:55:09: | x_1[0] | 0.1 | 0.25 | 0.4 | float |
INFO - 10:55:09: | x_1[1] | 0.75 | 1 | 1.25 | float |
INFO - 10:55:09: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 10:55:09: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 10:55:09: +-------------+-------------+-------+-------------+-------+
INFO - 10:55:09: Solving optimization problem with algorithm SLSQP:
INFO - 10:55:09: 10%|█ | 1/10 [00:00<00:00, 9.04 it/sec, obj=-536]
INFO - 10:55:09: 20%|██ | 2/10 [00:00<00:01, 6.54 it/sec, obj=-2.12e+3]
WARNING - 10:55:09: MDAJacobi has reached its maximum number of iterations but the normed residual 1.7130677857005655e-05 is still above the tolerance 1e-06.
INFO - 10:55:09: 30%|███ | 3/10 [00:00<00:01, 5.55 it/sec, obj=-3.75e+3]
INFO - 10:55:09: 40%|████ | 4/10 [00:00<00:01, 5.31 it/sec, obj=-3.96e+3]
INFO - 10:55:10: 50%|█████ | 5/10 [00:00<00:00, 5.18 it/sec, obj=-3.96e+3]
INFO - 10:55:10: Optimization result:
INFO - 10:55:10: Optimizer info:
INFO - 10:55:10: Status: 8
INFO - 10:55:10: Message: Positive directional derivative for linesearch
INFO - 10:55:10: Number of calls to the objective function by the optimizer: 6
INFO - 10:55:10: Solution:
INFO - 10:55:10: The solution is feasible.
INFO - 10:55:10: Objective: -3963.408265187933
INFO - 10:55:10: Standardized constraints:
INFO - 10:55:10: g_1 = [-0.01806104 -0.03334642 -0.04424946 -0.0518346 -0.05732607 -0.13720865
INFO - 10:55:10: -0.10279135]
INFO - 10:55:10: g_2 = 3.333278582928756e-06
INFO - 10:55:10: g_3 = [-7.67181773e-01 -2.32818227e-01 8.30379541e-07 -1.83255000e-01]
INFO - 10:55:10: Design space:
INFO - 10:55:10: +-------------+-------------+---------------------+-------------+-------+
INFO - 10:55:10: | Name | Lower bound | Value | Upper bound | Type |
INFO - 10:55:10: +-------------+-------------+---------------------+-------------+-------+
INFO - 10:55:10: | x_shared[0] | 0.01 | 0.06000083331964572 | 0.09 | float |
INFO - 10:55:10: | x_shared[1] | 30000 | 60000 | 60000 | float |
INFO - 10:55:10: | x_shared[2] | 1.4 | 1.4 | 1.8 | float |
INFO - 10:55:10: | x_shared[3] | 2.5 | 2.5 | 8.5 | float |
INFO - 10:55:10: | x_shared[4] | 40 | 70 | 70 | float |
INFO - 10:55:10: | x_shared[5] | 500 | 1500 | 1500 | float |
INFO - 10:55:10: | x_1[0] | 0.1 | 0.4 | 0.4 | float |
INFO - 10:55:10: | x_1[1] | 0.75 | 0.75 | 1.25 | float |
INFO - 10:55:10: | x_2 | 0.75 | 0.75 | 1.25 | float |
INFO - 10:55:10: | x_3 | 0.1 | 0.1562448753887276 | 1 | float |
INFO - 10:55:10: +-------------+-------------+---------------------+-------------+-------+
INFO - 10:55:10: *** End MDOScenario execution (time: 0:00:01.105724) ***
{'max_iter': 10, 'algo': 'SLSQP'}
Post-process scenario¶
Lastly, we post-process the scenario by means of the
ConstraintsHistory
plot which plots the history of constraints
passed as argument by the user. Each constraint history is represented by
a subplot where the value of the constraints is drawn by a line. Moreover,
the background color represents a qualitative view of these values: active
areas are white, violated ones are red and satisfied ones are green.
Tip
Each post-processing method requires different inputs and offers a variety
of customization options. Use the API function
get_post_processing_options_schema()
to print a table with
the options for any post-processing algorithm.
Or refer to our dedicated page:
Post-processing algorithms.
scenario.post_process(
"ConstraintsHistory",
constraint_names=all_constraints,
save=False,
show=True,
)
<gemseo.post.constraints_history.ConstraintsHistory object at 0x7f1dd6672cd0>
Total running time of the script: (0 minutes 2.293 seconds)