gemseo.mlearning.regression.algos.pce_settings module#

Settings of the polynomial chaos expansion model.

class CleaningOptions(max_considered_terms=100, most_significant=20, significance_factor=0.0001)[source]#

Bases: object

The options of the CleaningStrategy.

Parameters:

  • max_considered_terms (int) --

    By default it is set to 100.

  • most_significant (int) --

    By default it is set to 20.

  • significance_factor (float) --

    By default it is set to 0.0001.

max_considered_terms: int = 100#

The maximum number of coefficients of the polynomial basis to be considered.

most_significant: int = 20#

The maximum number of efficient coefficients of the polynomial basis to be kept.

significance_factor: float = 0.0001#

The threshold to select the efficient coefficients of the polynomial basis.

Settings PCERegressor_Settings(*, transformer=<factory>, parameters=<factory>, input_names=(), output_names=(), probability_space=None, degree=2, discipline=None, use_quadrature=False, use_lars=False, use_cleaning=False, hyperbolic_parameter=1.0, n_quadrature_points=0, cleaning_options=None)[source]#

Bases: BaseRegressorSettings

The settings of the polynomial chaos expansion model.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

  • transformer (Mapping[str, Any]) --

    By default it is set to <factory>.

  • parameters (Mapping[str, Any]) --

    By default it is set to <factory>.

  • input_names (Sequence[str]) --

    By default it is set to ().

  • output_names (Sequence[str]) --

    By default it is set to ().

  • probability_space (ParameterSpace | None)

  • degree (Annotated[int, Gt(gt=0)]) --

    By default it is set to 2.

  • discipline (Discipline | None)

  • use_quadrature (bool) --

    By default it is set to False.

  • use_lars (bool) --

    By default it is set to False.

  • use_cleaning (bool) --

    By default it is set to False.

  • hyperbolic_parameter (Annotated[float, Gt(gt=0)]) --

    By default it is set to 1.0.

  • n_quadrature_points (Annotated[int, Ge(ge=0)]) --

    By default it is set to 0.

  • cleaning_options (CleaningOptions | None)

Return type:

None

cleaning_options: CleaningOptions | None = None#

The options of the CleaningStrategy.

If None, use DEFAULT_CLEANING_OPTIONS.

degree: PositiveInt = 2#

The polynomial degree of the PCE.

Constraints:

  • gt = 0

discipline: Discipline | None = None#

The discipline to be sampled.

Used only when use_quadrature is True and data is None.

hyperbolic_parameter: PositiveFloat = 1.0#

The \(q\)-quasi norm parameter of the `hyperbolic and anisotropic enumerate function`_, defined over the interval:math:]0,1].

Constraints:

  • gt = 0

n_quadrature_points: NonNegativeInt = 0#

The total number of quadrature points.

These points are used to compute the marginal number of points by input dimension when discipline is not None. If 0, use \((1+P)^d\) points, where \(d\) is the dimension of the input space and \(P\) is the polynomial degree of the PCE.

Constraints:

  • ge = 0

probability_space: ParameterSpace | None = None#

The random input variables using OTDistribution.

If None, PCERegressor uses data.misc["input_space"] where data is the IODataset passed at instantiation.

use_cleaning: bool = False#

Whether to use the CleaningStrategy algorithm.

Otherwise, use a fixed truncation strategy (FixedStrategy).

use_lars: bool = False#

Whether to use the LARS algorithm.

This argument is ignored when use_quadrature is True.

use_quadrature: bool = False#

Whether to estimate the coefficients of the PCE by quadrature.

If so, use the quadrature points stored in data or sample discipline. Otherwise, estimate the coefficients by least-squares regression.

model_post_init(context, /)#

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that's what pydantic-core passes when calling it.

Parameters:

  • self (BaseModel) -- The BaseModel instance.

  • context (Any) -- The context.

Return type:

None