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

Classes:

ScalableProblem(datasets, design_variables, …)

Scalable problem.
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.

  • eq_constraints – inequality constraints. Default: None.

  • maximize_objective (bool) – maximize objective. Default: False.

  • sizes (dict) – sizes of input and output variables. If None, use the original sizes. Default: None.

  • parameters – optional parameters for the scalable model.

Methods:

create_scenario([formulation, …])

Create MDO scenario from the scalable disciplines.

exec_time([do_sum])

Get total execution time per discipline.

plot_1d_interpolations([save, show, step, …])

Plot 1d interpolations.

plot_coupling_graph()

Plot a coupling graph.

plot_dependencies([save, show, directory])

Plot dependency matrices.

plot_n2_chart([save, show])

Plot a N2 chart.

Attributes:

is_feasible

Get the feasibility property of the scenario.

n_calls

Get number of disciplinary calls per discipline.

n_calls_linearize

Get number of disciplinary calls per discipline.

n_calls_linearize_top_level

Get number of top level disciplinary calls per discipline.

n_calls_top_level

Get number of top level disciplinary calls per discipline.

status

Get the status of the scenario.
create_scenario(formulation='DisciplinaryOpt', scenario_type='MDO', start_at_equilibrium=False, active_probability=0.1, feasibility_level=0.5, **options)[source]

Create MDO scenario from the scalable disciplines.

Parameters

  • formulation (str) – MDO formulation. Default: ‘DisciplinaryOpt’.

  • scenario_type (str) – type of scenario (‘MDO’ or ‘DOE’). Default: ‘MDO’.

  • start_at_equilibrium (bool) – start at equilibrium using a preliminary MDA. Default: True.

  • active_probability (float) – probability to set the inequality constraints as active at initial step of the optimization. Default: 0.1.

  • feasibility_level (float) – offset of satisfaction for inequality constraints. Default: 0.5.

  • options – formulation options.

exec_time(do_sum=True)[source]

Get total execution time per discipline.

Parameters

do_sum (bool) – sum over disciplines (default: True)

Returns

execution time

Return type

list(float) or float

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

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.

  • show (bool) – show plot. Default: False.

  • step (bool) – Step to evaluate the 1d interpolation function Default: 0.01.

  • varnames (list(str)) – names of the variable to plot; if None, all variables are plotted. Default: None.

  • directory (str) – directory path. Default: ‘.’.

  • png (bool) – if True, the file format is PNG. Otherwise, use PDF. Default: 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)

  • show (bool) – show plot (default: False)

  • directory (str) – directory path (default: ‘.’)

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

Plot a N2 chart.

Parameters

  • save (bool) – save plot. Default: True.

  • show (bool) – show plot. Default: False.

property status

Get the status of the scenario.