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Simple disciplinary DOE example on the Sobieski SSBJ test case#
from __future__ import annotations
from gemseo import configure_logger
from gemseo import create_discipline
from gemseo import create_scenario
from gemseo.problems.mdo.sobieski.core.design_space import SobieskiDesignSpace
configure_logger()
<RootLogger root (INFO)>
Instantiate the discipline#
discipline = create_discipline("SobieskiMission")
Create the design space#
design_space = SobieskiDesignSpace()
design_space.filter(["y_24", "y_34"])
Create the scenario#
Build scenario which links the disciplines with the formulation and The DOE algorithm.
scenario = create_scenario(
[discipline],
"y_4",
design_space,
maximize_objective=True,
scenario_type="DOE",
formulation_name="DisciplinaryOpt",
)
Execute the scenario#
Here we use a latin hypercube sampling algorithm with 30 samples.
scenario.execute(algo_name="PYDOE_LHS", n_samples=30)
INFO - 20:38:57: *** Start DOEScenario execution ***
INFO - 20:38:57: DOEScenario
INFO - 20:38:57: Disciplines: SobieskiMission
INFO - 20:38:57: MDO formulation: DisciplinaryOpt
INFO - 20:38:57: Optimization problem:
INFO - 20:38:57: minimize -y_4(y_24, y_34)
INFO - 20:38:57: with respect to y_24, y_34
INFO - 20:38:57: over the design space:
INFO - 20:38:57: +------+-------------+------------+-------------+-------+
INFO - 20:38:57: | Name | Lower bound | Value | Upper bound | Type |
INFO - 20:38:57: +------+-------------+------------+-------------+-------+
INFO - 20:38:57: | y_24 | 0.44 | 4.15006276 | 11.13 | float |
INFO - 20:38:57: | y_34 | 0.44 | 1.10754577 | 1.98 | float |
INFO - 20:38:57: +------+-------------+------------+-------------+-------+
INFO - 20:38:57: Solving optimization problem with algorithm PYDOE_LHS:
INFO - 20:38:57: 3%|▎ | 1/30 [00:00<00:00, 374.49 it/sec, obj=-1.53e+3]
INFO - 20:38:57: 7%|▋ | 2/30 [00:00<00:00, 666.98 it/sec, obj=-1.66e+3]
INFO - 20:38:57: 10%|█ | 3/30 [00:00<00:00, 916.92 it/sec, obj=-832]
INFO - 20:38:57: 13%|█▎ | 4/30 [00:00<00:00, 1138.44 it/sec, obj=-1.62e+3]
INFO - 20:38:57: 17%|█▋ | 5/30 [00:00<00:00, 1337.38 it/sec, obj=-994]
INFO - 20:38:57: 20%|██ | 6/30 [00:00<00:00, 1515.37 it/sec, obj=-601]
INFO - 20:38:57: 23%|██▎ | 7/30 [00:00<00:00, 1678.39 it/sec, obj=-180]
INFO - 20:38:57: 27%|██▋ | 8/30 [00:00<00:00, 1817.19 it/sec, obj=-755]
INFO - 20:38:57: 30%|███ | 9/30 [00:00<00:00, 1952.25 it/sec, obj=-691]
INFO - 20:38:57: 33%|███▎ | 10/30 [00:00<00:00, 2064.53 it/sec, obj=-393]
INFO - 20:38:57: 37%|███▋ | 11/30 [00:00<00:00, 2169.74 it/sec, obj=-362]
INFO - 20:38:57: 40%|████ | 12/30 [00:00<00:00, 2263.83 it/sec, obj=-748]
INFO - 20:38:57: 43%|████▎ | 13/30 [00:00<00:00, 2357.37 it/sec, obj=-719]
INFO - 20:38:57: 47%|████▋ | 14/30 [00:00<00:00, 2446.68 it/sec, obj=-293]
INFO - 20:38:57: 50%|█████ | 15/30 [00:00<00:00, 2529.53 it/sec, obj=-931]
INFO - 20:38:57: 53%|█████▎ | 16/30 [00:00<00:00, 2606.07 it/sec, obj=-264]
INFO - 20:38:57: 57%|█████▋ | 17/30 [00:00<00:00, 2663.35 it/sec, obj=-1.17e+3]
INFO - 20:38:57: 60%|██████ | 18/30 [00:00<00:00, 2729.09 it/sec, obj=-495]
INFO - 20:38:57: 63%|██████▎ | 19/30 [00:00<00:00, 2785.84 it/sec, obj=-189]
INFO - 20:38:57: 67%|██████▋ | 20/30 [00:00<00:00, 2848.42 it/sec, obj=-2.23e+3]
INFO - 20:38:57: 70%|███████ | 21/30 [00:00<00:00, 2897.29 it/sec, obj=-344]
INFO - 20:38:57: 73%|███████▎ | 22/30 [00:00<00:00, 2946.10 it/sec, obj=-799]
INFO - 20:38:57: 77%|███████▋ | 23/30 [00:00<00:00, 2996.86 it/sec, obj=-55.9]
INFO - 20:38:57: 80%|████████ | 24/30 [00:00<00:00, 3047.54 it/sec, obj=-123]
INFO - 20:38:57: 83%|████████▎ | 25/30 [00:00<00:00, 3097.26 it/sec, obj=-875]
INFO - 20:38:57: 87%|████████▋ | 26/30 [00:00<00:00, 3135.39 it/sec, obj=-726]
INFO - 20:38:57: 90%|█████████ | 27/30 [00:00<00:00, 3178.39 it/sec, obj=-69.6]
INFO - 20:38:57: 93%|█████████▎| 28/30 [00:00<00:00, 3220.37 it/sec, obj=-1.51e+3]
INFO - 20:38:57: 97%|█████████▋| 29/30 [00:00<00:00, 3259.15 it/sec, obj=-1.15e+3]
INFO - 20:38:57: 100%|██████████| 30/30 [00:00<00:00, 3296.46 it/sec, obj=-2.73e+3]
INFO - 20:38:57: Optimization result:
INFO - 20:38:57: Optimizer info:
INFO - 20:38:57: Status: None
INFO - 20:38:57: Message: None
INFO - 20:38:57: Number of calls to the objective function by the optimizer: 0
INFO - 20:38:57: Solution:
INFO - 20:38:57: Objective: -2726.3660548732214
INFO - 20:38:57: Design space:
INFO - 20:38:57: +------+-------------+--------------------+-------------+-------+
INFO - 20:38:57: | Name | Lower bound | Value | Upper bound | Type |
INFO - 20:38:57: +------+-------------+--------------------+-------------+-------+
INFO - 20:38:57: | y_24 | 0.44 | 9.094543945649603 | 11.13 | float |
INFO - 20:38:57: | y_34 | 0.44 | 0.4769766573300308 | 1.98 | float |
INFO - 20:38:57: +------+-------------+--------------------+-------------+-------+
INFO - 20:38:57: *** End DOEScenario execution (time: 0:00:00.011560) ***
Note that both the formulation settings passed to func:.create_scenario and the
algorithm settings passed to execute() can be provided via a Pydantic model. For
more information, see Formulation Settings and Algorithm Settings.
Plot optimization history view#
scenario.post_process(post_name="OptHistoryView", save=False, show=True)
<gemseo.post.opt_history_view.OptHistoryView object at 0x785917d774a0>
Note that post-processor settings passed to BaseScenario.post_process() can be
provided via a Pydantic model (see the example below). For more information,
see Post-processor Settings.
Plot scatter plot matrix#
from gemseo.settings.post import ScatterPlotMatrix_Settings # noqa: E402
settings_model = ScatterPlotMatrix_Settings(
variable_names=["y_4", "y_24", "y_34"],
save=False,
show=True,
)
scenario.post_process(settings_model)
<gemseo.post.scatter_plot_matrix.ScatterPlotMatrix object at 0x78592d42e180>
Total running time of the script: (0 minutes 0.622 seconds)