Simple disciplinary DOE example on the Sobieski SSBJ test case

from __future__ import division, unicode_literals

from matplotlib import pyplot as plt

from gemseo.api import configure_logger, create_discipline, create_scenario
from gemseo.problems.sobieski.core import SobieskiProblem

configure_logger()

Out:

<RootLogger root (INFO)>

Instantiate the discipline

discipline = create_discipline("SobieskiMission")

Create the design space

design_space = SobieskiProblem().read_design_space()
design_space.filter(["y_24", "y_34"])

Out:

<gemseo.algos.design_space.DesignSpace object at 0x7f3b596edbe0>

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"})

Out:

    INFO - 14:40:34:
    INFO - 14:40:34: *** Start DOE Scenario execution ***
    INFO - 14:40:34: DOEScenario
    INFO - 14:40:34:    Disciplines: SobieskiMission
    INFO - 14:40:34:    MDOFormulation: DisciplinaryOpt
    INFO - 14:40:34:    Algorithm: lhs
    INFO - 14:40:34: Optimization problem:
    INFO - 14:40:34:    Minimize: -y_4(y_24, y_34)
    INFO - 14:40:34:    With respect to: y_24, y_34
    INFO - 14:40:34: DOE sampling:   0%|          | 0/30 [00:00<?, ?it]
    INFO - 14:40:34: DOE sampling: 100%|██████████| 30/30 [00:00<00:00, 1778.70 it/sec, obj=2.73e+3]
    INFO - 14:40:34: Optimization result:
    INFO - 14:40:34: Objective value = 2726.3660548732214
    INFO - 14:40:34: The result is feasible.
    INFO - 14:40:34: Status: None
    INFO - 14:40:34: Optimizer message: None
    INFO - 14:40:34: Number of calls to the objective function by the optimizer: 30
    INFO - 14:40:34: Design space:
    INFO - 14:40:34: +------+-------------+--------------------+-------------+-------+
    INFO - 14:40:34: | name | lower_bound |       value        | upper_bound | type  |
    INFO - 14:40:34: +------+-------------+--------------------+-------------+-------+
    INFO - 14:40:34: | y_24 |     0.44    | 9.094543945649603  |    11.13    | float |
    INFO - 14:40:34: | y_34 |     0.44    | 0.4769766573300308 |     1.98    | float |
    INFO - 14:40:34: +------+-------------+--------------------+-------------+-------+
    INFO - 14:40:34: *** DOE Scenario run terminated ***

{'eval_jac': False, 'algo': 'lhs', 'n_samples': 30}

Plot optimization history view

scenario.post_process("OptHistoryView", save=False, show=False)
  • Evolution of the optimization variables
  • Evolution of the objective value
  • Distance to the optimum

Out:

<gemseo.post.opt_history_view.OptHistoryView object at 0x7f3b597e0370>

Plot parallel coordinates

scenario.post_process(
    "ScatterPlotMatrix", save=False, show=False, variables_list=["y_4", "y_24", "y_34"]
)
# Workaround for HTML rendering, instead of ``show=True``
plt.show()
plot sobieski doe disc example

Total running time of the script: ( 0 minutes 1.167 seconds)

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