gemseo_mlearning / adaptive

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criterion module

Acquisition criterion for which the optimum would improve the regression model.

An acquisition criterion (also called infill criterion) is a function taking a model input value and returning a value of interest to maximize (default option) or minimize according to the meaning of the acquisition criterion.

Then, the input value optimizing this criterion can be used to enrich the dataset used by a machine learning algorithm in its training stage. This is the purpose of adaptive learning.

This notion of acquisition criterion is implemented through the [MLDataAcquisitionCriterion][gemseo_mlearning.adaptive.criterion.MLDataAcquisitionCriterion] class which is built from a [MLSupervisedAlgo][gemseo.mlearning.regression.regression.MLRegressionAlgo] and inherits from [MDOFunction][gemseo.core.mdofunctions.mdo_function.MDOFunction].

class gemseo_mlearning.adaptive.criterion.MLDataAcquisitionCriterion(algo_distribution, **options)[source]

Bases: MDOFunction

Acquisition criterion.

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

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

  • **options (MLDataAcquisitionCriterionOptionType) – The acquisition criterion options.

MAXIMIZE: ClassVar[bool] = True

Whether this criterion must be maximized.

algo_distribution: MLRegressorDistribution

The distribution of a machine learning algorithm assessor.

force_real: bool

Whether to cast the results to real value.

has_default_name: bool

Whether the name has been set with a default value.

last_eval: OutputType | None

The value of the function output at the last evaluation.

None if it has not yet been evaluated.

output_range: float

The output range.

special_repr: str

The string representation of the function overloading its default string ones.

class gemseo_mlearning.adaptive.criterion.MLDataAcquisitionCriterionFactory[source]

Bases: BaseFactory

A factory of MLDataAcquisitionCriterion.

Return type:


property available_criteria: list[str]

The names of the available criteria.

failed_imports: dict[str, str]

The class names bound to the import errors.