gemseo / problems / scalable / data_driven

problem module

Scalable MDO problem

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

Given

  • a 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.

Constructor.

Parameters

  • datasets (list(Dataset)) – disciplinary datasets.

  • design_variables (list(str)) – list of design variable names

  • objective_function (str) – objective function

  • eq_constraints (list(str)) –

    equality constraints. Default: None.

    By default it is set to None.

  • eq_constraints –

    inequality constraints. Default: None.

    By default it is set to None.

  • maximize_objective (bool) –

    maximize objective. Default: False.

    By default it is set to False.

  • sizes (dict) –

    sizes of input and output variables. If None, use the original sizes. Default: None.

    By default it is set to None.

  • parameters – optional parameters for 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

gemseo.core.scenario.Scenario

exec_time(do_sum=True)[source]

Get total execution time per discipline.

Parameters

do_sum (bool) –

sum over disciplines (default: True)

By default it is set to True.

Returns

execution time

Return type

list(float) or float

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

Plot 1d interpolations.

Parameters

  • save (bool) –

    save plot. Default: True.

    By default it is set to True.

  • show (bool) –

    show plot. Default: False.

    By default it is set to False.

  • step (bool) –

    Step to evaluate the 1d interpolation function Default: 0.01.

    By default it is set to 0.01.

  • varnames (list(str)) –

    names of the variable to plot; if None, all variables are plotted. Default: None.

    By default it is set to None.

  • directory (str) –

    directory path. Default: ‘.’.

    By default it is set to ..

  • png (bool) –

    if True, the file format is PNG. Otherwise, use PDF. Default: False.

    By default it is set to False.

plot_coupling_graph()[source]

Plot a coupling graph.

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

Plot dependency matrices.

Parameters

  • save (bool) –

    save plot (default: True)

    By default it is set to True.

  • show (bool) –

    show plot (default: False)

    By default it is set to False.

  • directory (str) –

    directory path (default: ‘.’)

    By default it is set to ..

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

Plot a N2 chart.

Parameters

  • save (bool) –

    save plot. Default: True.

    By default it is set to True.

  • show (bool) –

    show plot. Default: False.

    By default it is set to False.

property is_feasible

Get the feasibility property of the scenario.

property n_calls

Get number of disciplinary calls per discipline.

Returns

number of disciplinary calls per discipline

Return type

list(int) or int

property n_calls_linearize

Get number of disciplinary calls per discipline.

Returns

number of disciplinary calls per discipline

Return type

list(int) or int

property n_calls_linearize_top_level

Get number of top level disciplinary calls per discipline.

Returns

number of top level disciplinary calls per discipline

Return type

list(int) or int

property n_calls_top_level

Get number of top level disciplinary calls per discipline.

Returns

number of top level disciplinary calls per discipline

Return type

list(int) or int

property status

Get the status of the scenario.