gemseo / problems / scalable / data_driven

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discipline module

Scalable discipline.

The discipline implements the concept of scalable discipline. This is a particular discipline built from an input-output learning dataset associated with a function and generalizing its behavior to a new user-defined problem dimension, that is to say new user-defined input and output dimensions.

Alone or in interaction with other objects of the same type, a scalable discipline can be used to compare the efficiency of an algorithm applying to disciplines with respect to the problem dimension, e.g. optimization algorithm, surrogate model, MDO formulation, MDA, …

The ScalableDiscipline class implements this concept. It inherits from the MDODiscipline class in such a way that it can easily be used in a Scenario. It is composed of a ScalableModel.

The user only needs to provide:

• the name of a class overloading ScalableModel,

• a dataset as an Dataset

• variables sizes as a dictionary whose keys are the names of inputs and outputs and values are their new sizes. If a variable is missing, its original size is considered.

The ScalableModel parameters can also be filled in, otherwise the model uses default values.

class gemseo.problems.scalable.data_driven.discipline.ScalableDiscipline(name, data, sizes=None, **parameters)

Bases: MDODiscipline

A scalable discipline.

Initialize self. See help(type(self)) for accurate signature.

Parameters:

• name (str | None) – The name of the class of the scalable model.

• data (IODataset) – The learning dataset.

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

• **parameters (Any) – The parameters for the model.

initialize_grammars(data)

Initialize input and output grammars from data names.

Parameters:

data (IODataset) – The learning dataset.

Return type:

None

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.

exec_for_lin: bool

Whether the last execution was due to a linearization.

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.

output_grammar: BaseGrammar

The output grammar.

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.

Examples using ScalableDiscipline

Scalable diagonal discipline

Scalable diagonal discipline