How to deal with post-processing#
In this section we describe the post processing features of GEMSEO, used to
analyze OptimizationResult, called the
optimization history.
What data to post-process?#
Post-processing features are applicable to any
OptimizationProblem that has been solved,
which may have been loaded from the disk.
In practice,
a
BaseScenarioinstance has anBaseMDOFormulationattribute,an
BaseMDOFormulationinstance has anOptimizationProblemattribute,an
OptimizationProbleminstance has anOptimizationResultattribute.
Illustration on the Sobieski use case#
The post-processing features are illustrated on MDO results obtained on the SSBJ use case,
using different types of formulation (MDF formulation, IDF formulation, ...)
The following code sets up and executes the problem. It is possible to try different types of MDO strategies by changing
the formulation value. For a detailed explanation on how to setup the case, please see
Application: Sobieski's Super-Sonic Business Jet (MDO).
from gemseo import create_discipline, create_scenario
formulation = 'MDF'
disciplines = create_discipline(["SobieskiPropulsion", "SobieskiAerodynamics",
"SobieskiMission", "SobieskiStructure"])
scenario = create_scenario(disciplines,
formulation=formulation,
objective_name="y_4",
maximize_objective=True,
design_space="design_space.csv")
scenario.set_differentiation_method("user")
for constraint in ["g_1","g_2","g_3"]:
scenario.add_constraint(constraint, 'ineq')
scenario.execute(algo_name="SLSQP", max_iter=10)
How to apply a post-process feature?#
From this scenario, we can apply any kind of post-processing dedicated to BaseScenario instances,
either by means of its
post_process()method:- BaseScenario.post_process(settings_model=None, **settings)[source]
Post-process the optimization history.
- Parameters:
settings_model (BasePostSettings | None) -- The post-processor settings as a Pydantic model. If
None, use**settings.**settings (Any) -- The post-processor settings, including the algorithm name (use the keyword
"post_name"). These arguments are ignored whensettings_modelis notNone.
- Returns:
The post-processor.
- Return type:
or by means of the
execute_post()API method:- execute_post(to_post_proc, settings_model=None, **settings)[source]
Post-process a result.
- Parameters:
to_post_proc (BaseScenario | OptimizationProblem | str | Path) -- The result to be post-processed, either a DOE scenario, an MDO scenario, an optimization problem or a path to an HDF file containing a saved optimization problem.
settings_model (BasePostSettings | None) -- The post-processor settings as a Pydantic model. If
None, use**settings.**settings (Any) -- The post-processor settings, including the algorithm name (use the keyword
"post_name"). These arguments are ignored whensettings_modelis notNone.
- Returns:
The post-processor.
- Return type:
Examples
>>> from gemseo import create_discipline, create_scenario, execute_post >>> from gemseo.problems.mdo.sellar.sellar_design_space import ( ... SellarDesignSpace, ... ) >>> disciplines = create_discipline(["Sellar1", "Sellar2", "SellarSystem"]) >>> design_space = SellarDesignSpace() >>> scenario = create_scenario( ... disciplines, ... "obj", ... design_space, ... formulation_name="MDF", ... name="SellarMDFScenario", ... ) >>> scenario.execute(algo_name="NLOPT_SLSQP", max_iter=100) >>> execute_post(scenario, post_name="OptHistoryView", show=False, save=True)
Note
Only design variables and functions (objective function, constraints) are stored for post-processing.
If you want to be able to plot state variables, you must add them as observables before the problem is executed.
Use the add_observable() method.
Algorithms#
The data used to illustrate the post-processing algorithms in the following examples are
from an MDO scenario on the Sobieski's SSBJ problem.
The scenario uses the MDF formulation and was solved using the SciPy SLSQP algorithm.
The data have been saved in an HDF5 file, which is passed to the high-level function
execute_post() along with the appropriate settings model.
Note
To get the specific settings of a given post-processing, one should use the
appropriate settings model accessible in gemseo.settings.post.
Further details on the available post-processings and their settings can be found in
the following dedicated page: Post-processing algorithms.
Details on how to execute such a scenario can be found here.
