gemseo / problems / scalable / data_driven

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problem module

Scalable MDO problem.

This module implements the concept of scalable problem by means of the ScalableProblem class.

Given

  • an MDO scenario based on a set of sampled disciplines with a particular problem dimension,

  • a new problem dimension (= number of inputs and outputs),

a scalable problem:

  1. makes each discipline scalable based on the new problem dimension,

  2. creates the corresponding MDO scenario.

Then, this MDO scenario can be executed and post-processed.

We can repeat this tasks for different sizes of variables and compare the scalability, which is the dependence of the scenario results on the problem dimension.

See also

MDODiscipline, ScalableDiscipline and Scenario

class gemseo.problems.scalable.data_driven.problem.ScalableProblem(datasets, design_variables, objective_function, eq_constraints=None, ineq_constraints=None, maximize_objective=False, sizes=None, **parameters)[source]

Bases: object

Scalable problem.

Parameters:

  • datasets (Iterable[IODataset]) – One input-output dataset per discipline.

  • design_variables (Iterable[str]) – The names of the design variables.

  • objective_function (str) – The name of the objective.

  • eq_constraints (Iterable[str] | None) – The names of the equality constraints, if any.

  • ineq_constraints (Iterable[str] | None) – The names of the inequality constraints, if any.

  • maximize_objective (bool) –

    Whether to maximize the objective.

    By default it is set to False.

  • sizes (Mapping[str, int] | None) – The sizes of the inputs and outputs. If None, use the original sizes.

  • **parameters (Any) – The optional parameters of the scalable model.

create_scenario(formulation='DisciplinaryOpt', scenario_type='MDO', start_at_equilibrium=False, active_probability=0.1, feasibility_level=0.5, **options)[source]

Create a Scenario from the scalable disciplines.

Parameters:

  • formulation (str) –

    The MDO formulation to use for the scenario.

    By default it is set to “DisciplinaryOpt”.

  • scenario_type (str) –

    The type of scenario, either MDO or DOE.

    By default it is set to “MDO”.

  • start_at_equilibrium (bool) –

    Whether to start at equilibrium using a preliminary MDA.

    By default it is set to False.

  • active_probability (float) –

    The probability to set the inequality constraints as active at the initial step of the optimization.

    By default it is set to 0.1.

  • feasibility_level (float) –

    The offset of satisfaction for inequality constraints.

    By default it is set to 0.5.

  • **options – The formulation options.

Returns:

The Scenario from the scalable disciplines.

Return type:

Scenario

exec_time(do_sum=True)[source]

Get the total execution time.

Parameters:

do_sum (bool) –

Whether to sum the disciplinary execution times.

By default it is set to True.

Returns:

Either the total execution time or the total execution times per disciplines.

Return type:

float | list[float]

plot_1d_interpolations(save=True, show=False, step=0.01, varnames=None, directory='.', png=False)[source]

Plot 1d interpolations.

Parameters:

  • save (bool) –

    Whether to save the figure.

    By default it is set to True.

  • show (bool) –

    Whether to display the figure.

    By default it is set to False.

  • step (float) –

    The step to evaluate the 1d interpolation function.

    By default it is set to 0.01.

  • varnames (Sequence[str] | None) – The names of the variable to plot. If None, all the variables are plotted.

  • directory (Path | str) –

    The directory path.

    By default it is set to “.”.

  • png (bool) –

    Whether to use PNG file format instead of PDF.

    By default it is set to False.

plot_coupling_graph()[source]

Plot a coupling graph.

Return type:

None

plot_dependencies(save=True, show=False, directory='.')[source]

Plot dependency matrices.

Parameters:

  • save (bool) –

    Whether to save the figure.

    By default it is set to True.

  • show (bool) –

    Whether to display the figure.

    By default it is set to False.

  • directory (str) –

    The directory path.

    By default it is set to “.”.

plot_n2_chart(save=True, show=False)[source]

Plot a N2 chart.

Parameters:

  • save (bool) –

    Whether to save the figure.

    By default it is set to True.

  • show (bool) –

    Whether to display the figure.

    By default it is set to False.

Return type:

None

property is_feasible: bool

Whether the solution is feasible.

property n_calls: dict[str, int]

The number of disciplinary calls per discipline.

property n_calls_linearize: dict[str, int]

The number of disciplinary linearizations per discipline.

property n_calls_linearize_top_level: dict[str, int]

The number of top-level disciplinary linearizations per discipline.

property n_calls_top_level: dict[str, int]

The number of top-level disciplinary calls per discipline.

property status: int

The status of the scenario.

Examples using ScalableProblem

Scalable problem

Scalable problem