acquisition module¶
Acquisition of learning data from a machine learning algorithm and a criterion.
- class gemseo_mlearning.adaptive.acquisition.MLDataAcquisition(criterion, input_space, distribution, **options)[source]
Bases:
object
Data acquisition for adaptive learning.
# 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:
input_space (DesignSpace) – The input space on which to look for the new learning point.
distribution (MLRegressorDistribution) – The distribution of the machine learning algorithm.
**options (MLDataAcquisitionCriterionOptionType) – The options of the acquisition criterion.
criterion (str) –
- Raises:
NotImplementedError – When the output dimension is greater than 1.
- compute_next_input_data(as_dict=False)[source]
Find the next learning point.
- set_acquisition_algorithm(algo_name, **options)[source]
Set sampling or optimization algorithm.
- 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 (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:
tuple[gemseo.algos.database.Database, gemseo.algos.opt_problem.OptimizationProblem]
- update_problem()[source]
Update the optimization problem.
- Return type:
None
- default_algo_name: ClassVar[str] = 'NLOPT_COBYLA'
The name of the default algorithm to find the point(s).
Typically a DoE or an optimizer.