Note
Click here to download the full example code
Self-Organizing Map¶
In this example, we illustrate the use of the SOM
plot
on the Sobieski’s SSBJ problem.
from __future__ import absolute_import, division, print_function, unicode_literals
from future import standard_library
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()
standard_library.install_aliases()
Create disciplines¶
Then, we instantiate the disciplines of the 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 a 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 Monte Carlo DOE algorithm and 30 samples.
scenario = create_scenario(
disciplines,
formulation="MDF",
objective_name="y_4",
maximize_objective=True,
design_space=design_space,
scenario_type="DOE",
)
scenario.set_differentiation_method("user")
for constraint in ["g_1", "g_2", "g_3"]:
scenario.add_constraint(constraint, "ineq")
scenario.execute({"algo": "OT_MONTE_CARLO", "n_samples": 30})
Out:
{'eval_jac': False, 'algo': 'OT_MONTE_CARLO', 'n_samples': 30}
Post-process scenario¶
Lastly, we post-process the scenario by means of the
SOM
plot which performs a self organizing map
clustering on optimization history.
scenario.post_process("SOM", save=False, show=True)
Out:
<gemseo.post.som.SOM object at 0x7fc298921280>
Total running time of the script: ( 0 minutes 2.432 seconds)