macrosynergy.learning.forecasting.model_inference#

class LarsSurrogateModel(estimator, n_factors=3, eps=1e-06)[source]#

Bases: BaseEstimator, RegressorMixin

Surrogate model to infer the behaviour of a model through a LARS regression on the model’s predictions based on the input features.

Parameters:

  • estimator (sklearn regressor) – The model to be explained by the surrogate model.

  • n_factors (int, default=3) – The number of non-zero coefficients to select in the LARS regression.

  • eps (float, default=1e-6) – Machine precision for LARS regression.

Notes

Surrogate models are used to explain the behaviour of complex models by regressing a collection of features on the model’s predictions. A key property is that the model is explainable.

Future versions of this class will allow for a separate factor set to be used for inspection.

fit(X, y)[source]#
predict(X)[source]#
set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LarsSurrogateModel#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

Submodules#