gemseo_mlearning / adaptive / criteria / value

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criterion module

Expected improvement of the regression model for a particular value.

Statistics:

\[EI[x] = E[|q-Y(x)|]\]

where \(q\) is a value provided by the user.

Bootstrap estimator:

\[\widehat{EI}[x] = \frac{1}{B}\sum_{b=1}^B |q-Y_b(x)|\]

class gemseo_mlearning.adaptive.criteria.value.criterion.LimitState(algo_distribution, value)

Bases: MLDataAcquisitionCriterion

Expected Improvement of the regression model for a particular value.

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

Parameters:

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

• value (float) – A value of interest.

MAXIMIZE: ClassVar[bool] = False

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.

value: float

The value of interest.