gemseo.mlearning.regression.algos.base_fce module#

Base class for functional chaos expansion models.

class BaseFCERegressor(data, settings_model=None, **settings)[source]#

Base class for functional chaos expansion models.

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

Parameters:

data (IODataset) -- The training dataset whose input space data.misc["input_space"] is expected to be a ParameterSpace defining the random input variables.
settings_model (BaseFCERegressor_Settings | None) -- The machine learning algorithm settings as a Pydantic model. If None, use **settings.
**settings (Any) -- The machine learning algorithm settings. These arguments are ignored when settings_model is not None.

Raises:

ValueError -- When learn_jacobian_data or use_special_jacobian_data is True but the training dataset does not contain Jacobian data.

property first_sobol_indices: list[dict[str, RealArray]]#: The first-order Sobol' indices for the different output components.

Warning

These statistics are expressed in relation to the transformed output space. You can use a SobolAnalysis to estimate them in relation to the original output space if it is different from the transformed output space.

property mean: RealArray#: The mean vector of the model output.

Warning

This statistic is expressed in relation to the transformed output space. You can sample the predict() method to estimate it in relation to the original output space if it is different from the transformed output space.

property mean_jacobian_wrt_special_variables: RealArray#

The gradient of the mean with respect to the special variables.

See predict_jacobian_wrt_special_variables() for more information about the notion of special variables.

Raises:: ValueError -- When the training dataset does not include gradient information.

property standard_deviation: RealArray#: The standard deviation vector of the model output.

Warning

This statistic is expressed in relation to the transformed output space. You can sample the predict() method to estimate it in relation to the original output space if it is different from the transformed output space.

property standard_deviation_jacobian_wrt_special_variables: RealArray#

The gradient of the standard deviation with respect to the special variables.

See predict_jacobian_wrt_special_variables() for more information about the notion of special variables.

Raises:: ValueError -- When the training dataset does not include gradient information.

property total_sobol_indices: list[dict[str, RealArray]]#: The total Sobol' indices for the different output components.

Warning

These statistics are expressed in relation to the transformed output space. You can use a SobolAnalysis to estimate them in relation to the original output space if it is different from the transformed output space.

property variance: RealArray#: The variance vector of the model output.

Warning

This statistic is expressed in relation to the transformed output space. You can sample the predict() method to estimate it in relation to the original output space if it is different from the transformed output space.

property variance_jacobian_wrt_special_variables: RealArray#

The gradient of the variance with respect to the special variables.

See predict_jacobian_wrt_special_variables() for more information about the notion of special variables.

Raises:: ValueError -- When the training dataset does not include gradient information.