Note
Click here to download the full example code
Gradient Sensitivity¶
In this example, we illustrate the use of the GradientSensitivity
plot on the Sobieski’s SSBJ problem.
from __future__ import division, unicode_literals
from matplotlib import pyplot as plt
Import¶
The first step is to import some functions from the API and a method to get the design space.
from gemseo.api import configure_logger, create_discipline, create_scenario
from gemseo.problems.sobieski.core import SobieskiProblem
configure_logger()
Out:
<RootLogger root (INFO)>
Description¶
The GradientSensitivity post-processing builds histograms of derivatives of the objective and the 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 read the design space from the SobieskiProblem
.
design_space = SobieskiProblem().read_design_space()
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("finite_differences")
for constraint in ["g_1", "g_2", "g_3"]:
scenario.add_constraint(constraint, "ineq")
scenario.execute({"algo": "SLSQP", "max_iter": 10})
Out:
INFO - 14:41:58:
INFO - 14:41:58: *** Start MDO Scenario execution ***
INFO - 14:41:58: MDOScenario
INFO - 14:41:58: Disciplines: SobieskiPropulsion SobieskiAerodynamics SobieskiStructure SobieskiMission
INFO - 14:41:58: MDOFormulation: MDF
INFO - 14:41:58: Algorithm: SLSQP
INFO - 14:41:58: Optimization problem:
INFO - 14:41:58: Minimize: -y_4(x_shared, x_1, x_2, x_3)
INFO - 14:41:58: With respect to: x_shared, x_1, x_2, x_3
INFO - 14:41:58: Subject to constraints:
INFO - 14:41:58: g_1(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 14:41:58: g_2(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 14:41:58: g_3(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 14:41:58: Design space:
INFO - 14:41:58: +----------+-------------+-------+-------------+-------+
INFO - 14:41:58: | name | lower_bound | value | upper_bound | type |
INFO - 14:41:58: +----------+-------------+-------+-------------+-------+
INFO - 14:41:58: | x_shared | 0.01 | 0.05 | 0.09 | float |
INFO - 14:41:58: | x_shared | 30000 | 45000 | 60000 | float |
INFO - 14:41:58: | x_shared | 1.4 | 1.6 | 1.8 | float |
INFO - 14:41:58: | x_shared | 2.5 | 5.5 | 8.5 | float |
INFO - 14:41:58: | x_shared | 40 | 55 | 70 | float |
INFO - 14:41:58: | x_shared | 500 | 1000 | 1500 | float |
INFO - 14:41:58: | x_1 | 0.1 | 0.25 | 0.4 | float |
INFO - 14:41:58: | x_1 | 0.75 | 1 | 1.25 | float |
INFO - 14:41:58: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 14:41:58: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 14:41:58: +----------+-------------+-------+-------------+-------+
INFO - 14:41:58: Optimization: 0%| | 0/10 [00:00<?, ?it]
INFO - 14:42:00: Optimization: 20%|██ | 2/10 [00:01<00:01, 5.30 it/sec, obj=2.12e+3]
INFO - 14:42:02: Optimization: 30%|███ | 3/10 [00:04<00:03, 2.22 it/sec, obj=3.79e+3]
INFO - 14:42:06: Optimization: 40%|████ | 4/10 [00:08<00:05, 1.19 it/sec, obj=4.01e+3]
WARNING - 14:42:09: MDAJacobi has reached its maximum number of iterations but the normed residual 1.377945654084145e-06 is still above the tolerance 1e-06.
INFO - 14:42:09: Optimization: 50%|█████ | 5/10 [00:11<00:00, 52.57 it/min, obj=4.51e+3]
WARNING - 14:42:09: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3778434716154599e-06 is still above the tolerance 1e-06.
WARNING - 14:42:10: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3780025526238928e-06 is still above the tolerance 1e-06.
WARNING - 14:42:10: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3779485439447035e-06 is still above the tolerance 1e-06.
WARNING - 14:42:10: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3776904668827516e-06 is still above the tolerance 1e-06.
WARNING - 14:42:10: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3781026103129487e-06 is still above the tolerance 1e-06.
WARNING - 14:42:10: MDAJacobi has reached its maximum number of iterations but the normed residual 1.377742640146233e-06 is still above the tolerance 1e-06.
WARNING - 14:42:10: MDAJacobi has reached its maximum number of iterations but the normed residual 1.377997922984005e-06 is still above the tolerance 1e-06.
WARNING - 14:42:10: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3782574002965124e-06 is still above the tolerance 1e-06.
WARNING - 14:42:10: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3779467160619637e-06 is still above the tolerance 1e-06.
WARNING - 14:42:10: MDAJacobi has reached its maximum number of iterations but the normed residual 1.376763131250477e-06 is still above the tolerance 1e-06.
WARNING - 14:42:10: MDAJacobi has reached its maximum number of iterations but the normed residual 1.377945654084145e-06 is still above the tolerance 1e-06.
WARNING - 14:42:10: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3778434716154599e-06 is still above the tolerance 1e-06.
WARNING - 14:42:11: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3780025526238928e-06 is still above the tolerance 1e-06.
WARNING - 14:42:11: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3779485439447035e-06 is still above the tolerance 1e-06.
WARNING - 14:42:11: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3776904668827516e-06 is still above the tolerance 1e-06.
WARNING - 14:42:11: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3781026103129487e-06 is still above the tolerance 1e-06.
WARNING - 14:42:11: MDAJacobi has reached its maximum number of iterations but the normed residual 1.377742640146233e-06 is still above the tolerance 1e-06.
WARNING - 14:42:11: MDAJacobi has reached its maximum number of iterations but the normed residual 1.377997922984005e-06 is still above the tolerance 1e-06.
WARNING - 14:42:11: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3782574002965124e-06 is still above the tolerance 1e-06.
WARNING - 14:42:11: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3779467160619637e-06 is still above the tolerance 1e-06.
WARNING - 14:42:11: MDAJacobi has reached its maximum number of iterations but the normed residual 1.376763131250477e-06 is still above the tolerance 1e-06.
WARNING - 14:42:11: MDAJacobi has reached its maximum number of iterations but the normed residual 1.377945654084145e-06 is still above the tolerance 1e-06.
WARNING - 14:42:11: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3778434716154599e-06 is still above the tolerance 1e-06.
WARNING - 14:42:12: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3780025526238928e-06 is still above the tolerance 1e-06.
WARNING - 14:42:12: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3779485439447035e-06 is still above the tolerance 1e-06.
WARNING - 14:42:12: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3776904668827516e-06 is still above the tolerance 1e-06.
WARNING - 14:42:12: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3781026103129487e-06 is still above the tolerance 1e-06.
WARNING - 14:42:12: MDAJacobi has reached its maximum number of iterations but the normed residual 1.377742640146233e-06 is still above the tolerance 1e-06.
WARNING - 14:42:12: MDAJacobi has reached its maximum number of iterations but the normed residual 1.377997922984005e-06 is still above the tolerance 1e-06.
WARNING - 14:42:12: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3782574002965124e-06 is still above the tolerance 1e-06.
WARNING - 14:42:12: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3779467160619637e-06 is still above the tolerance 1e-06.
WARNING - 14:42:12: MDAJacobi has reached its maximum number of iterations but the normed residual 1.376763131250477e-06 is still above the tolerance 1e-06.
WARNING - 14:42:12: MDAJacobi has reached its maximum number of iterations but the normed residual 1.377945654084145e-06 is still above the tolerance 1e-06.
WARNING - 14:42:12: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3778434716154599e-06 is still above the tolerance 1e-06.
WARNING - 14:42:12: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3780025526238928e-06 is still above the tolerance 1e-06.
WARNING - 14:42:13: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3779485439447035e-06 is still above the tolerance 1e-06.
WARNING - 14:42:13: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3776904668827516e-06 is still above the tolerance 1e-06.
WARNING - 14:42:13: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3781026103129487e-06 is still above the tolerance 1e-06.
WARNING - 14:42:13: MDAJacobi has reached its maximum number of iterations but the normed residual 1.377742640146233e-06 is still above the tolerance 1e-06.
WARNING - 14:42:13: MDAJacobi has reached its maximum number of iterations but the normed residual 1.377997922984005e-06 is still above the tolerance 1e-06.
WARNING - 14:42:13: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3782574002965124e-06 is still above the tolerance 1e-06.
WARNING - 14:42:13: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3779467160619637e-06 is still above the tolerance 1e-06.
WARNING - 14:42:13: MDAJacobi has reached its maximum number of iterations but the normed residual 1.376763131250477e-06 is still above the tolerance 1e-06.
INFO - 14:42:13: Optimization: 60%|██████ | 6/10 [00:15<00:00, 39.07 it/min, obj=4.52e+3]
WARNING - 14:42:13: MDAJacobi has reached its maximum number of iterations but the normed residual 1.1177878724838186e-06 is still above the tolerance 1e-06.
WARNING - 14:42:13: MDAJacobi has reached its maximum number of iterations but the normed residual 1.2441814738218321e-06 is still above the tolerance 1e-06.
INFO - 14:42:13: Optimization: 80%|████████ | 8/10 [00:15<00:00, 38.61 it/min, obj=4.51e+3]
WARNING - 14:42:14: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3097841302240645e-06 is still above the tolerance 1e-06.
WARNING - 14:42:14: MDAJacobi has reached its maximum number of iterations but the normed residual 1.3435518312233814e-06 is still above the tolerance 1e-06.
INFO - 14:42:14: Optimization: 100%|██████████| 10/10 [00:15<00:00, 38.16 it/min, obj=4.51e+3]
WARNING - 14:42:14: Optimization found no feasible point ! The least infeasible point is selected.
INFO - 14:42:14: Optimization: 100%|██████████| 10/10 [00:15<00:00, 38.15 it/min, obj=4.51e+3]
INFO - 14:42:14: Optimization result:
INFO - 14:42:14: Objective value = 3789.3185446884972
INFO - 14:42:14: The result is not feasible.
INFO - 14:42:14: Status: None
INFO - 14:42:14: Optimizer message: Maximum number of iterations reached. GEMSEO Stopped the driver
INFO - 14:42:14: Number of calls to the objective function by the optimizer: 12
INFO - 14:42:14: Constraints values:
INFO - 14:42:14: g_1 = [-0.01968861 -0.03451033 -0.04515197 -0.0525708 -0.05794746 -0.13703141
INFO - 14:42:14: -0.10296859]
INFO - 14:42:14: g_2 = 0.0004732410401158127
INFO - 14:42:14: g_3 = [-0.62555576 -0.37444424 -0.14466936 -0.18313783]
INFO - 14:42:14: Design space:
INFO - 14:42:14: +----------+-------------+---------------------+-------------+-------+
INFO - 14:42:14: | name | lower_bound | value | upper_bound | type |
INFO - 14:42:14: +----------+-------------+---------------------+-------------+-------+
INFO - 14:42:14: | x_shared | 0.01 | 0.06011831026002896 | 0.09 | float |
INFO - 14:42:14: | x_shared | 30000 | 60000 | 60000 | float |
INFO - 14:42:14: | x_shared | 1.4 | 1.400900029003249 | 1.8 | float |
INFO - 14:42:14: | x_shared | 2.5 | 2.5 | 8.5 | float |
INFO - 14:42:14: | x_shared | 40 | 70 | 70 | float |
INFO - 14:42:14: | x_shared | 500 | 1500 | 1500 | float |
INFO - 14:42:14: | x_1 | 0.1 | 0.3989644630096731 | 0.4 | float |
INFO - 14:42:14: | x_1 | 0.75 | 0.75 | 1.25 | float |
INFO - 14:42:14: | x_2 | 0.75 | 0.75 | 1.25 | float |
INFO - 14:42:14: | x_3 | 0.1 | 0.1337468319957488 | 1 | float |
INFO - 14:42:14: +----------+-------------+---------------------+-------------+-------+
INFO - 14:42:14: *** MDO Scenario run terminated in 0:00:15.735393 ***
{'algo': 'SLSQP', 'max_iter': 10}
Post-process scenario¶
Lastly, we post-process the scenario by means of the GradientSensitivity
plot which builds histograms of derivatives of objective and constraints.
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:
Options for Post-processing algorithms.
scenario.post_process("GradientSensitivity", save=False, show=False)
# Workaround for HTML rendering, instead of ``show=True``
plt.show()
Out:
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.2.2/lib/python3.8/site-packages/gemseo/post/gradient_sensitivity.py:163: UserWarning: FixedFormatter should only be used together with FixedLocator
axe.set_xticklabels(x_labels, fontsize=12, rotation=90)
Total running time of the script: ( 0 minutes 16.464 seconds)