macrosynergy.learning.forecasting.meta_estimators.weighted_predictors#

class TimeWeightedWrapper(model, half_life)[source]#

Bases: BaseEstimator, RegressorMixin

Meta-estimator that applies time-based weighting to samples during model fitting.

Parameters:

  • model (BaseEstimator) – An instance of a scikit-learn compatible regression model.

  • half_life (float) – The half-life parameter for the exponential decay weighting.

fit(X, y)[source]#

Fit the underlying model with time weights applied.

Parameters:
predict(X)[source]#

Predict using the underlying model.

Parameters:

X (pandas.DataFrame or np.ndarray) – The feature matrix.

Returns:

predictions – The predicted values.

Return type:

np.ndarray

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TimeWeightedWrapper#

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