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] | None) – The names of the input variables. If None, consider all the input variables of the learning dataset.

  • output_names (Iterable[str] | None) – 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 description is missing.

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: Dataset

The learning dataset.

output_names: list[str]

The names of the output variables.

parameters: dict[str, MLAlgoParameterType]

The parameters of the machine learning algorithm.

resampling_results: dict[str, tuple[Resampler, list[MLAlgo], list[ndarray] | ndarray]]

The resampler class names bound to the resampling results.

A resampling result is formatted as (resampler, ml_algos, predictions) where resampler is a Resampler, ml_algos is the list of the associated machine learning algorithms built during the resampling stage and predictions are the predictions obtained with the latter.

resampling_results stores only one resampling result per resampler type (e.g., "CrossValidation", "LeaveOneOut" and "Boostrap").

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.