scenario module¶
The base class for the scenarios.
- class gemseo.core.scenario.Scenario(disciplines, formulation, objective_name, design_space, name=None, grammar_type=GrammarType.JSON, maximize_objective=False, **formulation_options)[source]
Bases:
MDODiscipline
Base class for the scenarios.
The instantiation of a
Scenario
creates anOptimizationProblem
, by linkingMDODiscipline
objects with anMDOFormulation
and defining both the objective to minimize or maximize and theDesignSpace
on which to solve the problem. Constraints can also be added to theOptimizationProblem
with theScenario.add_constraint()
method, as well as observables with theScenario.add_observable()
method.Then, the
Scenario.execute()
method takes a driver (seeDriverLibrary
) 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 byScenario.posts
.Initialize self. See help(type(self)) for accurate signature.
- Parameters:
disciplines (Sequence[MDODiscipline]) – The disciplines used to compute the objective, constraints and observables from the design variables.
formulation (str) – The class name of the
MDOFormulation
, e.g."MDF"
,"IDF"
or"BiLevel"
.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 | None) – The name to be given to this scenario. If
None
, use the name of the class.grammar_type (MDODiscipline.GrammarType) –
The grammar for the scenario and the MDO formulation.
By default it is set to “JSONGrammar”.
maximize_objective (bool) –
Whether to maximize the objective.
By default it is set to False.
**formulation_options (Any) – The options of the
MDOFormulation
.
- 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'
- add_constraint(output_name, constraint_type=ConstraintType.EQ, constraint_name=None, value=None, positive=False, **kwargs)[source]
Add a design constraint.
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 names of the outputs to be used as constraints. For instance, if “g_1” is given and constraint_type=”eq”, g_1=0 will be added as constraint to the optimizer. 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 | None) – The name of the constraint to be stored. If
None
, the name of the constraint is generated from the output name.value (float | None) – The value for which the constraint is active. If
None
, this value is 0.positive (bool) –
If
True
, 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=None, 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 (Sequence[str] | None) – The name to be given to the observable. If
None
, the output name is used by default.discipline (MDODiscipline | None) – The discipline used to build the observable function. If
None
, detect the discipline from the inner disciplines.
- Return type:
None
- get_disciplines_statuses()[source]
Retrieve the statuses of the disciplines.
- get_expected_dataflow()[source]
Return the expected data exchange sequence.
This method is used for the XDSM representation.
The default expected data exchange sequence is an empty list.
See also
MDOFormulation.get_expected_dataflow
- Returns:
The data exchange arcs.
- Return type:
- get_expected_workflow()[source]
Return the expected execution sequence.
This method is used for the XDSM representation.
The default expected execution sequence is the execution of the discipline itself.
See also
MDOFormulation.get_expected_workflow
- Returns:
The expected execution sequence.
- Return type:
- get_optim_variable_names()[source]
A convenience function to access the optimization variables.
- 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 (seecreate_scenario_result()
).By default it is set to “”.
**options (Any) – The options of the
ScenarioResult
.
- Returns:
The result of the scenario execution.
- Return type:
- post_process(post_name, **options)[source]
Post-process the optimization history.
- Parameters:
post_name (str) – The name of the post-processor, i.e. the name of a class inheriting from
OptPostProcessor
.**options (OptPostProcessorOptionType | Path) – The options for the post-processor.
- Returns:
The post-processing instance related to the optimization scenario.
- Return type:
- 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='hdf5', append=False)[source]
Save the optimization history of the scenario to a file.
- Parameters:
- Raises:
ValueError – If the file format is not correct.
- 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
theDesignSpace
current value will be cast tocomplex128
; additionally, if the optioncast_default_inputs_to_complex
isTrue
, the default inputs of the scenario’s disciplines will be cast as well provided that they arendarray
withdtype
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, each_new_iter=False, each_store=True, erase=False, pre_load=False, generate_opt_plot=False)[source]
Set the backup file for the optimization history during the run.
- Parameters:
file_path (str | Path) – The path to the file to save the history.
each_new_iter (bool) –
Whether the backup file is updated at every iteration of the optimization to store the database.
By default it is set to False.
each_store (bool) –
Whether the backup file is updated at every function call to store the database.
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.
pre_load (bool) –
Whether the backup file is loaded before run, useful after a crash.
By default it is set to False.
generate_opt_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
andpre_load
areTrue
.- 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
orDataset.INPUT_GROUP
for the design variables andDataset.FUNCTION_GROUP
orDataset.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
andDataset.FUNCTION_GROUP
as groups. Otherwise, useDataset.INPUT_GROUP
andDataset.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:
- 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. If
show_html=True
andoutput_directory_path=None
, the HTML file is stored in a temporary directory.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
isFalse
.- Return type:
XDSM | None
- ALGO = 'algo'
- ALGO_OPTIONS = 'algo_options'
- L_BOUNDS = 'l_bounds'
- U_BOUNDS = 'u_bounds'
- X_0 = 'x_0'
- activate_input_data_check: ClassVar[bool] = True
Whether to check the input data respect the input grammar.
- activate_output_data_check: ClassVar[bool] = True
Whether to check the output data respect the output grammar.
- cache: AbstractCache | None
The cache containing one or several executions of the discipline according to the cache policy.
- data_processor: DataProcessor
A tool to pre- and post-process discipline data.
- property design_space: DesignSpace
The design space on which the scenario is performed.
- exec_for_lin: bool
Whether the last execution was due to a linearization.
- formulation: MDOFormulation
The MDO formulation.
- formulation_name: str
The name of the MDO formulation.
- input_grammar: BaseGrammar
The input grammar.
- jac: MutableMapping[str, MutableMapping[str, ndarray | csr_array | JacobianOperator]]
The Jacobians of the outputs wrt inputs.
The structure is
{output: {input: matrix}}
.
- name: str
The name of the discipline.
- optimization_result: OptimizationResult | None
The optimization result if the scenario has been executed; otherwise
None
.
- output_grammar: BaseGrammar
The output grammar.
- property post_factory: PostFactory
The factory for post-processors if any.
- re_exec_policy: ReExecutionPolicy
The policy to re-execute the same discipline.
- residual_variables: dict[str, str]
The output variables mapping to their inputs, to be considered as residuals; they shall be equal to zero.
- run_solves_residuals: bool
Whether the run method shall solve the residuals.
- property use_standardized_objective: bool
Whether to use the standardized objective for logging and post-processing.
The objective is
OptimizationProblem.objective
.