gemseo.scenarios.base_scenario module#

The base class for the scenarios.

class BaseScenario(disciplines, objective_name, design_space, name='', maximize_objective=False, formulation_settings_model=None, **formulation_settings)[source]#

Bases: BaseMonitoredProcess

Base class for the scenarios.

The instantiation of a Scenario creates an OptimizationProblem, by linking Discipline objects with an BaseMDOFormulation and defining both the objective to minimize or maximize and the DesignSpace on which to solve the problem. Constraints can also be added to the OptimizationProblem with the Scenario.add_constraint() method, as well as observables with the Scenario.add_observable() method.

Then, the Scenario.execute() method takes a driver (see BaseDriverLibrary) with options as input data and uses it to solve the optimization problem. This driver is in charge of executing the multidisciplinary process.

To view the results, use the Scenario.post_process() method after execution with one of the available post-processors that can be listed by Scenario.posts.

Initialize self. See help(type(self)) for accurate signature.

Parameters:

  • disciplines (Sequence[Discipline]) -- The disciplines used to compute the objective, constraints and observables from the design variables.

  • objective_name (str | Sequence[str]) -- The name(s) of the discipline output(s) used as objective. If multiple names are passed, the objective will be a vector.

  • design_space (DesignSpace) -- The search space including at least the design variables (some formulations requires additional variables, e.g. IDF with the coupling variables).

  • name (str) --

    The name to be given to this scenario. If empty, use the name of the class.

    By default it is set to "".

  • maximize_objective (bool) --

    Whether to maximize the objective.

    By default it is set to False.

  • formulation_settings_model (BaseFormulationSettings | None) -- The formulation settings as a Pydantic model. If None, use **settings.

  • **formulation_settings (Any) -- The formulation settings, including the formulation name (use the keyword "formulation_name"). These arguments are ignored when settings_model is not None.

class DifferentiationMethod(value)#

Bases: StrEnum

The differentiation methods.

CENTERED_DIFFERENCES = 'centered_differences'#
COMPLEX_STEP = 'complex_step'#
FINITE_DIFFERENCES = 'finite_differences'#
NO_DERIVATIVE = 'no_derivative'#
USER_GRAD = 'user'#
Settings#

The class used to validate the arguments of execute().

alias of _BaseSettings

add_constraint(output_name, constraint_type=ConstraintType.EQ, constraint_name='', value=0, positive=False, **kwargs)[source]#

Add an equality or inequality constraint to the optimization problem.

An equality constraint is written as \(c(x)=a\), a positive inequality constraint is written as \(c(x)\geq a\) and a negative inequality constraint is written as \(c(x)\leq a\).

This constraint is in addition to those created by the formulation, e.g. consistency constraints in IDF.

The strategy of repartition of the constraints is defined by the formulation.

Parameters:

  • output_name (str | Sequence[str]) -- The name(s) of the outputs computed by \(c(x)\). If several names are given, a single discipline must provide all outputs.

  • constraint_type (MDOFunction.ConstraintType) --

    The type of constraint.

    By default it is set to "eq".

  • constraint_name (str) --

    The name of the constraint to be stored. If empty, the name of the constraint is generated from output_name, constraint_type, value and positive.

    By default it is set to "".

  • value (float) --

    The value \(a\).

    By default it is set to 0.

  • positive (bool) --

    Whether the inequality constraint is positive.

    By default it is set to False.

Raises:

ValueError -- If the constraint type is neither 'eq' nor 'ineq'.

Return type:

None

add_observable(output_names, observable_name='', discipline=None)[source]#

Add an observable to the optimization problem.

The repartition strategy of the observable is defined in the formulation class. When more than one output name is provided, the observable function returns a concatenated array of the output values.

Parameters:

  • output_names (Sequence[str]) -- The names of the outputs to observe.

  • observable_name (str) --

    The name to be given to the observable. If empty, the output name is used by default.

    By default it is set to "".

  • discipline (Discipline | None) -- The discipline used to build the observable function. If None, detect the discipline from the inner disciplines.

Return type:

None

execute(algo_settings_model=None, **algo_settings)[source]#

Execute a scenario.

Parameters:

  • algo_settings_model (BaseDriverSettings | None) -- The algorithm settings as a Pydantic model. If None, use **settings if any. If None and no settings, the method will use the settings defined by set_algorithm().

  • **algo_settings (Any) -- The algorithm settings, including the algorithm name (use the keyword "algo_name"). These arguments are ignored when settings_model is not None.

Return type:

None

get_optim_variable_names()[source]#

A convenience function to access the optimization variables.

Returns:

The optimization variables of the scenario.

Return type:

list[str]

get_result(name='', **options)[source]#

Return the result of the scenario execution.

Parameters:

  • name (str) --

    The class name of the ScenarioResult. If empty, use a default one (see create_scenario_result()).

    By default it is set to "".

  • **options (Any) -- The options of the ScenarioResult.

Returns:

The result of the scenario execution.

Return type:

ScenarioResult | None

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 when settings_model is not None.

Returns:

The post-processor.

Return type:

BasePost

print_execution_metrics()[source]#

Print the total number of executions and cumulated runtime by discipline.

Return type:

None

save_optimization_history(file_path, file_format=HistoryFileFormat.HDF5, append=False)[source]#

Save the optimization history of the scenario to a file.

Parameters:

  • file_path (str | Path) -- The path of the file to save the history.

  • file_format (OptimizationProblem.HistoryFileFormat) --

    The format of the file.

    By default it is set to "hdf5".

  • append (bool) --

    If True, the history is appended to the file if not empty.

    By default it is set to False.

Return type:

None

set_algorithm(algo_settings_model=None, **algo_settings)[source]#

Define the algorithm to execute the scenario.

Parameters:

  • algo_settings_model (BaseDriverSettings | None) -- The algorithm settings as a Pydantic model. If None, use **settings.

  • **algo_settings (Any) -- The algorithm settings, including the algorithm name (use the keyword "algo_name"). These arguments are ignored when settings_model is not None.

Return type:

None

set_differentiation_method(method=DifferentiationMethod.USER_GRAD, step=1e-06, cast_default_inputs_to_complex=False)[source]#

Set the differentiation method for the process.

When the selected method to differentiate the process is complex_step the DesignSpace current value will be cast to complex128; additionally, if the option cast_default_inputs_to_complex is True, the default inputs of the scenario's disciplines will be cast as well provided that they are ndarray with dtype float64.

Parameters:

  • method (DifferentiationMethod) --

    The method to use to differentiate the process.

    By default it is set to "user".

  • step (float) --

    The finite difference step.

    By default it is set to 1e-06.

  • cast_default_inputs_to_complex (bool) --

    Whether to cast all float default inputs of the scenario's disciplines if the selected method is "complex_step".

    By default it is set to False.

Return type:

None

set_optimization_history_backup(file_path, at_each_iteration=False, at_each_function_call=True, erase=False, load=False, plot=False)[source]#

Set the backup file to store the evaluations of the functions during the run.

Parameters:

  • file_path (str | Path) -- The backup file path.

  • at_each_iteration (bool) --

    Whether the backup file is updated at every iteration of the optimization.

    By default it is set to False.

  • at_each_function_call (bool) --

    Whether the backup file is updated at every function call.

    By default it is set to True.

  • erase (bool) --

    Whether the backup file is erased before the run.

    By default it is set to False.

  • load (bool) --

    Whether the backup file is loaded before run, useful after a crash.

    By default it is set to False.

  • plot (bool) --

    Whether to plot the optimization history view at each iteration. The plots will be generated only after the first two iterations.

    By default it is set to False.

Raises:

ValueError -- If both erase and pre_load are True.

Return type:

None

to_dataset(name='', categorize=True, opt_naming=True, export_gradients=False)[source]#

Export the database of the optimization problem to a Dataset.

The variables can be classified into groups: Dataset.DESIGN_GROUP or Dataset.INPUT_GROUP for the design variables and Dataset.FUNCTION_GROUP or Dataset.OUTPUT_GROUP for the functions (objective, constraints and observables).

Parameters:

  • name (str) --

    The name to be given to the dataset. If empty, use the name of the OptimizationProblem.database.

    By default it is set to "".

  • categorize (bool) --

    Whether to distinguish between the different groups of variables. Otherwise, group all the variables in Dataset.PARAMETER_GROUP`.

    By default it is set to True.

  • opt_naming (bool) --

    Whether to use Dataset.DESIGN_GROUP and Dataset.FUNCTION_GROUP as groups. Otherwise, use Dataset.INPUT_GROUP and Dataset.OUTPUT_GROUP.

    By default it is set to True.

  • export_gradients (bool) --

    Whether to export the gradients of the functions (objective function, constraints and observables) if the latter are available in the database of the optimization problem.

    By default it is set to False.

Returns:

A dataset built from the database of the optimization problem.

Return type:

Dataset

xdsmize(monitor=False, directory_path='.', log_workflow_status=False, file_name='xdsm', show_html=False, save_html=True, save_json=False, save_pdf=False, pdf_build=True, pdf_cleanup=True, pdf_batchmode=True)[source]#

Create a XDSM diagram of the scenario.

Parameters:

  • monitor (bool) --

    Whether to update the generated file at each discipline status change.

    By default it is set to False.

  • log_workflow_status (bool) --

    Whether to log the evolution of the workflow's status.

    By default it is set to False.

  • directory_path (str | Path) --

    The path of the directory to save the files.

    By default it is set to ".".

  • file_name (str) --

    The file name without the file extension.

    By default it is set to "xdsm".

  • show_html (bool) --

    Whether to open the web browser and display the XDSM.

    By default it is set to False.

  • save_html (bool) --

    Whether to save the XDSM as a HTML file.

    By default it is set to True.

  • save_json (bool) --

    Whether to save the XDSM as a JSON file.

    By default it is set to False.

  • save_pdf (bool) --

    Whether to save the XDSM as a PDF file.

    By default it is set to False.

  • pdf_build (bool) --

    Whether the standalone pdf of the XDSM will be built.

    By default it is set to True.

  • pdf_cleanup (bool) --

    Whether pdflatex built files will be cleaned up after build is complete.

    By default it is set to True.

  • pdf_batchmode (bool) --

    Whether pdflatex is run in batchmode.

    By default it is set to True.

Returns:

A view of the XDSM if monitor is False.

Return type:

XDSM | None

clear_history_before_execute: bool#

Whether to clear the history before execute.

property design_space: DesignSpace#

The design space on which the scenario is performed.

property disciplines: tuple[BaseDiscipline, ...]#

The disciplines.

formulation: BaseMDOFormulation#

The MDO formulation.

formulation_name: str#

The name of the MDO formulation.

optimization_result: OptimizationResult | None#

The optimization result if the scenario has been executed; otherwise None.

property post_factory: PostFactory#

The factory for post-processors if any.

property posts: list[str]#

The available post-processors.

property use_standardized_objective: bool#

Whether to use the standardized objective for logging and post-processing.

The objective is OptimizationProblem.objective.