idf module¶
The Individual Discipline Feasible (IDF) formulation.
- class gemseo.formulations.idf.IDF(disciplines, objective_name, design_space, maximize_objective=False, normalize_constraints=True, n_processes=1, use_threading=True, start_at_equilibrium=False, grammar_type=GrammarType.JSON)[source]¶
Bases:
MDOFormulation
The Individual Discipline Feasible (IDF) formulation.
This formulation draws an optimization architecture where the coupling variables of strongly coupled disciplines is made consistent by adding equality constraints on the coupling variables at top level, the optimization problem with respect to the local, global design variables and coupling variables is made at the top level.
The disciplinary analysis is made at each optimization iteration while the multidisciplinary analysis is made at the optimum.
- Parameters:
disciplines (list[MDODiscipline]) – The disciplines.
objective_name (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 design space.
maximize_objective (bool) –
Whether to maximize the objective.
By default it is set to False.
normalize_constraints (bool) –
If
True
, the outputs of the coupling consistency constraints are scaled.By default it is set to True.
n_processes (int) –
The maximum simultaneous number of threads, if
use_threading
is True, or processes otherwise, used to parallelize the execution.By default it is set to 1.
use_threading (bool) –
Whether to use threads instead of processes to parallelize the execution; multiprocessing will copy (serialize) all the disciplines, while threading will share all the memory. This is important to note if you want to execute the same discipline multiple times, you shall use multiprocessing.
By default it is set to True.
start_at_equilibrium (bool) –
If
True
, an MDA is used to initialize the coupling variables.By default it is set to False.
grammar_type (MDODiscipline.GrammarType) –
The type of the input and output grammars.
By default it is set to “JSONGrammar”.
- add_constraint(output_name, constraint_type=ConstraintType.EQ, constraint_name=None, value=None, positive=False)¶
Add a user constraint.
A user constraint is a design constraint in addition to the formulation specific constraints such as the targets (a.k.a. consistency constraints) in IDF.
The strategy of repartition of constraints is defined in the formulation class.
- Parameters:
output_name (str) – The name of the output to be used as a constraint. For instance, if g_1 is given and constraint_type=”eq”, g_1=0 will be added as a constraint to the optimizer.
constraint_type (MDOFunction.ConstraintType) –
The type of constraint, either “eq” for equality constraint or “ineq” for inequality constraint.
By default it is set to “eq”.
constraint_name (str | None) – The name of the constraint to be stored, If
None
, the name is generated from the output name.value (float | None) – The value of activation of the constraint. If
None
, the value is equal to 0.positive (bool) –
Whether to consider an inequality constraint as positive.
By default it is set to False.
- Return type:
None
- add_observable(output_names, observable_name=None, discipline=None)¶
Add an observable to the optimization problem.
The repartition strategy of the observable is defined in the formulation class.
- Parameters:
output_names (str | Sequence[str]) – The name(s) of the output(s) to observe.
observable_name (str | None) – The name of the observable.
discipline (MDODiscipline | None) – The discipline computing the observed outputs. If
None
, the discipline is detected from inner disciplines.
- Return type:
None
- classmethod get_default_sub_option_values(**options)¶
Return the default values of the sub-options of the formulation.
When some options of the formulation depend on higher level options, the default values of these sub-options may be obtained here, mainly for use in the API.
- get_expected_dataflow()[source]¶
Get the expected data exchange sequence.
This method is used for the XDSM representation and can be overloaded by subclasses.
- Returns:
The expected sequence of data exchange where the i-th item is described by the starting discipline, the ending discipline and the coupling variables.
- Return type:
- get_expected_workflow()[source]¶
Get the expected sequence of execution of the disciplines.
This method is used for the XDSM representation and can be overloaded by subclasses.
For instance:
[A, B] denotes the execution of A, then the execution of B
(A, B) denotes the concurrent execution of A and B
[A, (B, C), D] denotes the execution of A, then the concurrent execution of B and C, then the execution of D.
- Returns:
A sequence of elements which are either an
ExecutionSequence
or a tuple ofExecutionSequence
for concurrent execution.- Return type:
- get_optim_variable_names()¶
Get the optimization unknown names to be provided to the optimizer.
This is different from the design variable names provided by the user, since it depends on the formulation, and can include target values for coupling for instance in IDF.
- get_sub_disciplines(recursive=False)¶
Accessor to the sub-disciplines.
This method lists the sub scenarios’ disciplines. It will list up to one level of disciplines contained inside another one unless the
recursive
argument is set toTrue
.- Parameters:
recursive (bool) –
If
True
, the method will look inside any discipline that has other disciplines inside until it reaches a discipline without sub-disciplines, in this case the return value will not include any discipline that has sub-disciplines. IfFalse
, the method will list up to one level of disciplines contained inside another one, in this case the return value may include disciplines that contain sub-disciplines.By default it is set to False.
- Returns:
The sub-disciplines.
- Return type:
- classmethod get_sub_options_grammar(**options)¶
Get the sub-options grammar.
When some options of the formulation depend on higher level options, the schema of the sub-options may be obtained here, mainly for use in the API.
- Parameters:
**options (str) – The options required to deduce the sub-options grammar.
- Returns:
Either
None
or the sub-options grammar.- Return type:
- get_sub_scenarios()¶
List the disciplines that are actually scenarios.
- get_top_level_disc()[source]¶
Return the disciplines which inputs are required to run the scenario.
A formulation seeks to compute the objective and constraints from the input variables. It structures the optimization problem into multiple levels of disciplines. The disciplines directly depending on these inputs are called top level disciplines.
By default, this method returns all disciplines. This method can be overloaded by subclasses.
- Returns:
The top level disciplines.
- Return type:
- get_x_mask_x_swap_order(masking_data_names, all_data_names=None)¶
Mask a vector from a subset of names, with respect to a set of names.
This method eventually swaps the order of the values if the order of the data names is inconsistent between these sets.
- Parameters:
- Returns:
The masked version of the input vector.
- Raises:
ValueError – If the sizes or the sizes of variables are inconsistent.
- Return type:
ndarray
- get_x_names_of_disc(discipline)¶
Get the design variables names of a given discipline.
- Parameters:
discipline (MDODiscipline) – The discipline.
- Returns:
The names of the design variables.
- Return type:
- mask_x_swap_order(masking_data_names, x_vect, all_data_names=None)¶
Mask a vector from a subset of names, with respect to a set of names.
This method eventually swaps the order of the values if the order of the data names is inconsistent between these sets.
- Parameters:
- Returns:
The masked version of the input vector.
- Raises:
IndexError – when the sizes of variables are inconsistent.
- Return type:
ndarray
- unmask_x_swap_order(masking_data_names, x_masked, all_data_names=None, x_full=None)¶
Unmask a vector from a subset of names, with respect to a set of names.
This method eventually swaps the order of the values if the order of the data names is inconsistent between these sets.
- Parameters:
masking_data_names (Iterable[str]) – The names of the kept data.
x_masked (ndarray) – The boolean vector to unmask.
all_data_names (Iterable[str] | None) – The set of all names. If
None
, use the design variables stored in the design space.x_full (ndarray) – The default values for the full vector. If
None
, use the zero vector.
- Returns:
The vector related to the input mask.
- Raises:
IndexError – when the sizes of variables are inconsistent.
- Return type:
ndarray
- DEFAULT_SCENARIO_RESULT_CLASS_NAME: ClassVar[str] = 'ScenarioResult'¶
The name of the
ScenarioResult
class to be used for post-processing.
- property design_space: DesignSpace¶
The design space on which the formulation is applied.
- property disciplines: list[MDODiscipline]¶
The disciplines of the MDO process.
- opt_problem: OptimizationProblem¶
The optimization problem generated by the formulation from the disciplines.