gemseo_mlearning / adaptive

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acquisition module

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

class gemseo_mlearning.adaptive.acquisition.MLDataAcquisition(criterion, input_space, distribution, **criterion_options)[source]

Bases: object

Data acquisition for adaptive learning.

  • criterion (str) – The name of a data acquisition criterion selecting new point(s) to reach a particular goal (name of a class inheriting from MLDataAcquisitionCriterion).

  • input_space (DesignSpace) – The input space on which to look for the new learning point.

  • distribution (MLRegressorDistribution) – The distribution of the machine learning algorithm.

  • **criterion_options (MLDataAcquisitionCriterionOptionType) – The options of the data acquisition criterion.


NotImplementedError – When the output dimension is greater than 1.


Find the next learning point.


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.


The next learning point.

Return type:


set_acquisition_algorithm(algo_name, **options)[source]

Set sampling or optimization algorithm.

  • 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:


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.

  • 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.


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

Return type:

tuple[Database, OptimizationProblem]


Update the acquisition problem.

Return type:


default_algo_name: ClassVar[str] = 'NLOPT_COBYLA'

The name of the default algorithm to find the new training 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.