gemseo_mlearning / regression

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thin_plate_spline module

Thin plate spline regression.

class gemseo_mlearning.regression.thin_plate_spline.TPSRegressor(data, transformer=None, input_names=None, output_names=None, smooth=0.0, norm='euclidean', **parameters)[source]

Bases: RBFRegressor

Thin plate spline (TPS) regression.

Parameters:

  • data (Dataset) – The learning dataset.

  • transformer (Mapping[str, TransformerType] | None) – The strategies to transform the variables. The values are instances of Transformer while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the Transformer will be applied to all the variables of this group. If IDENTITY, do not transform the variables.

  • input_names (Iterable[str]) – The names of the input variables. If None, consider all the input variables of the learning dataset.

  • output_names (Iterable[str]) – The names of the output variables. If None, consider all the output variables of the learning dataset.

  • smooth (float) –

    The degree of smoothness, 0 involving an interpolation of the learning points.

    By default it is set to 0.0.

  • norm (str | Callable[[ndarray, ndarray], float]) –

    The distance metric to be used, either a distance function name known by SciPy or a function that computes the distance between two points.

    By default it is set to “euclidean”.

  • **parameters (Any) – The parameters of the machine learning algorithm.

Raises:

ValueError – When both the variable and the group it belongs to have a transformer.

SHORT_ALGO_NAME: ClassVar[str] = 'TPS'

The short name of the machine learning algorithm, often an acronym.

Typically used for composite names, e.g. f"{algo.SHORT_ALGO_NAME}_{dataset.name}" or f"{algo.SHORT_ALGO_NAME}_{discipline.name}".

algo: Any

The interfaced machine learning algorithm.

der_function: Callable[[ndarray], ndarray]

The derivative of the radial basis function.

input_names: list[str]

The names of the input variables.

input_space_center: dict[str, ndarray]

The center of the input space.

learning_set: IODataset

The learning dataset.

output_names: list[str]

The names of the output variables.

parameters: dict[str, MLAlgoParameterType]

The parameters of the machine learning algorithm.

transformer: dict[str, Transformer]

The strategies to transform the variables, if any.

The values are instances of Transformer while the keys are the names of either the variables or the groups of variables, e.g. “inputs” or “outputs” in the case of the regression algorithms. If a group is specified, the Transformer will be applied to all the variables of this group.

y_average: ndarray

The mean of the learning output data.