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Simple disciplinary DOE example on the Sobieski SSBJ test case¶
from __future__ import annotations
from gemseo.api import configure_logger
from gemseo.api import create_discipline
from gemseo.api import create_scenario
from gemseo.problems.sobieski.core.problem import SobieskiProblem
configure_logger()
<RootLogger root (INFO)>
Instantiate the discipline¶
discipline = create_discipline("SobieskiMission")
Create the design space¶
design_space = SobieskiProblem().design_space
design_space.filter(["y_24", "y_34"])
<gemseo.algos.design_space.DesignSpace object at 0x7f67896214f0>
Create the scenario¶
Build scenario which links the disciplines with the formulation and The DOE algorithm.
scenario = create_scenario(
[discipline],
formulation="DisciplinaryOpt",
objective_name="y_4",
design_space=design_space,
maximize_objective=True,
scenario_type="DOE",
)
Execute the scenario¶
Here we use a latin hypercube sampling algorithm with 30 samples.
scenario.execute({"n_samples": 30, "algo": "lhs"})
INFO - 13:29:51:
INFO - 13:29:51: *** Start DOEScenario execution ***
INFO - 13:29:51: DOEScenario
INFO - 13:29:51: Disciplines: SobieskiMission
INFO - 13:29:51: MDO formulation: DisciplinaryOpt
INFO - 13:29:51: Optimization problem:
INFO - 13:29:51: minimize -y_4(y_24, y_34) = -y_4(y_24, y_34)
INFO - 13:29:51: with respect to y_24, y_34
INFO - 13:29:51: over the design space:
INFO - 13:29:51: +------+-------------+------------+-------------+-------+
INFO - 13:29:51: | name | lower_bound | value | upper_bound | type |
INFO - 13:29:51: +------+-------------+------------+-------------+-------+
INFO - 13:29:51: | y_24 | 0.44 | 4.15006276 | 11.13 | float |
INFO - 13:29:51: | y_34 | 0.44 | 1.10754577 | 1.98 | float |
INFO - 13:29:51: +------+-------------+------------+-------------+-------+
INFO - 13:29:51: Solving optimization problem with algorithm lhs:
INFO - 13:29:51: ... 0%| | 0/30 [00:00<?, ?it]
INFO - 13:29:51: ... 3%|▎ | 1/30 [00:00<00:00, 268.69 it/sec, obj=-1.53e+3]
INFO - 13:29:51: ... 7%|▋ | 2/30 [00:00<00:00, 443.98 it/sec, obj=-1.66e+3]
INFO - 13:29:51: ... 10%|█ | 3/30 [00:00<00:00, 573.12 it/sec, obj=-832]
INFO - 13:29:51: ... 13%|█▎ | 4/30 [00:00<00:00, 682.81 it/sec, obj=-1.62e+3]
INFO - 13:29:51: ... 17%|█▋ | 5/30 [00:00<00:00, 766.56 it/sec, obj=-994]
INFO - 13:29:51: ... 20%|██ | 6/30 [00:00<00:00, 839.28 it/sec, obj=-601]
INFO - 13:29:51: ... 23%|██▎ | 7/30 [00:00<00:00, 901.28 it/sec, obj=-180]
INFO - 13:29:51: ... 27%|██▋ | 8/30 [00:00<00:00, 941.24 it/sec, obj=-755]
INFO - 13:29:51: ... 30%|███ | 9/30 [00:00<00:00, 976.63 it/sec, obj=-691]
INFO - 13:29:51: ... 33%|███▎ | 10/30 [00:00<00:00, 1016.65 it/sec, obj=-393]
INFO - 13:29:51: ... 37%|███▋ | 11/30 [00:00<00:00, 1049.15 it/sec, obj=-362]
INFO - 13:29:51: ... 40%|████ | 12/30 [00:00<00:00, 1080.50 it/sec, obj=-748]
INFO - 13:29:51: ... 43%|████▎ | 13/30 [00:00<00:00, 1108.84 it/sec, obj=-719]
INFO - 13:29:51: ... 47%|████▋ | 14/30 [00:00<00:00, 1124.78 it/sec, obj=-293]
INFO - 13:29:51: ... 50%|█████ | 15/30 [00:00<00:00, 1139.51 it/sec, obj=-931]
INFO - 13:29:51: ... 53%|█████▎ | 16/30 [00:00<00:00, 1161.07 it/sec, obj=-264]
INFO - 13:29:51: ... 57%|█████▋ | 17/30 [00:00<00:00, 1177.73 it/sec, obj=-1.17e+3]
INFO - 13:29:51: ... 60%|██████ | 18/30 [00:00<00:00, 1195.11 it/sec, obj=-495]
INFO - 13:29:51: ... 63%|██████▎ | 19/30 [00:00<00:00, 1211.49 it/sec, obj=-189]
INFO - 13:29:51: ... 67%|██████▋ | 20/30 [00:00<00:00, 1219.11 it/sec, obj=-2.23e+3]
INFO - 13:29:51: ... 70%|███████ | 21/30 [00:00<00:00, 1225.24 it/sec, obj=-344]
INFO - 13:29:51: ... 73%|███████▎ | 22/30 [00:00<00:00, 1237.14 it/sec, obj=-799]
INFO - 13:29:51: ... 77%|███████▋ | 23/30 [00:00<00:00, 1248.48 it/sec, obj=-55.9]
INFO - 13:29:51: ... 80%|████████ | 24/30 [00:00<00:00, 1260.37 it/sec, obj=-123]
INFO - 13:29:51: ... 83%|████████▎ | 25/30 [00:00<00:00, 1271.45 it/sec, obj=-875]
INFO - 13:29:51: ... 87%|████████▋ | 26/30 [00:00<00:00, 1275.39 it/sec, obj=-726]
INFO - 13:29:51: ... 90%|█████████ | 27/30 [00:00<00:00, 1278.20 it/sec, obj=-69.6]
INFO - 13:29:51: ... 93%|█████████▎| 28/30 [00:00<00:00, 1287.51 it/sec, obj=-1.51e+3]
INFO - 13:29:51: ... 97%|█████████▋| 29/30 [00:00<00:00, 1294.62 it/sec, obj=-1.15e+3]
INFO - 13:29:51: ... 100%|██████████| 30/30 [00:00<00:00, 1302.86 it/sec, obj=-2.73e+3]
INFO - 13:29:51: Optimization result:
INFO - 13:29:51: Optimizer info:
INFO - 13:29:51: Status: None
INFO - 13:29:51: Message: None
INFO - 13:29:51: Number of calls to the objective function by the optimizer: 30
INFO - 13:29:51: Solution:
INFO - 13:29:51: Objective: -2726.3660548732214
INFO - 13:29:51: Design space:
INFO - 13:29:51: +------+-------------+--------------------+-------------+-------+
INFO - 13:29:51: | name | lower_bound | value | upper_bound | type |
INFO - 13:29:51: +------+-------------+--------------------+-------------+-------+
INFO - 13:29:51: | y_24 | 0.44 | 9.094543945649603 | 11.13 | float |
INFO - 13:29:51: | y_34 | 0.44 | 0.4769766573300308 | 1.98 | float |
INFO - 13:29:51: +------+-------------+--------------------+-------------+-------+
INFO - 13:29:51: *** End DOEScenario execution (time: 0:00:00.032121) ***
{'eval_jac': False, 'n_samples': 30, 'algo': 'lhs'}
Plot optimization history view¶
scenario.post_process("OptHistoryView", save=False, show=True)
<gemseo.post.opt_history_view.OptHistoryView object at 0x7f676d655f70>
Plot parallel coordinates¶
scenario.post_process(
"ScatterPlotMatrix",
variable_names=["y_4", "y_24", "y_34"],
save=False,
show=True,
)

<gemseo.post.scatter_mat.ScatterPlotMatrix object at 0x7f676d6550d0>
Total running time of the script: ( 0 minutes 1.373 seconds)