# acquisition module¶

Acquisition of learning data from a machine learning algorithm and a criterion.

Bases: object

# noqa: D205 D212 D415 :param criterion: The name of a data acquisition criterion

selecting new point(s) to reach a particular goal (name of a class inheriting from MLDataAcquisitionCriterion).

Parameters
Raises

NotImplementedError – When the output dimension is greater than 1.

Return type

None

compute_next_input_data(as_dict=False)[source]

Find the next learning point.

Parameters

as_dict (bool) –

Whether to return the input data split by input names. Otherwise, return a unique array.

By default it is set to False.

Returns

The next learning point.

Return type

Union[numpy.ndarray, Mapping[str, numpy.ndarray]]

set_acquisition_algorithm(algo_name, **options)[source]

Set sampling or optimization algorithm.

Parameters
• algo_name (str) – The name of the algorithm to find the learning point(s). Typically a DoE or an optimizer.

• **options (Any) – The values of some algorithm options; use the default values for the other ones.

Return type

None

update_algo(discipline, n_samples=1)[source]

Update the machine learning algorithm by learning new samples.

This method acquires new learning input-output samples and trains the machine learning algorithm with the resulting enriched learning set.

Parameters
• discipline (gemseo.core.discipline.MDODiscipline) – The discipline computing the reference output data from the input data provided by the acquisition process.

• n_samples (int) –

The number of samples to update the machine learning algorithm.

By default it is set to 1.

Returns

The concatenation of the optimization histories related to the different points and the last optimization problem.

Return type
default_algo_name: ClassVar[str] = 'NLOPT_COBYLA'

The name of the default algorithm to find the point(s).

Typically a DoE or an optimizer.

default_doe_options: ClassVar[dict[str, Any]] = {'n_samples': 100}

The names and values of the default DoE options.

default_opt_options: ClassVar[dict[str, Any]] = {'max_iter': 100}

The names and values of the default optimization options.