macrosynergy.learning.forecasting.linear_model.ls_regressors.weighted_ls_regressors#

class SignWeightedLinearRegression(fit_intercept=True, positive=False, alpha=0, shrinkage_type='l1')[source]#

Bases: SignWeightedRegressor

set_params(**params)[source]#

Setter method to update the parameters of the SignWeightedLinearRegression.

Parameters:

**params (dict) – Dictionary of parameters to update.

Returns:

The SignWeightedLinearRegression instance with updated parameters.

Return type:

self

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

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

class TimeWeightedLinearRegression(fit_intercept=True, positive=False, half_life=252, alpha=0, shrinkage_type='l1')[source]#

Bases: TimeWeightedRegressor

set_params(**params)[source]#

Setter method to update the parameters of the TimeWeightedLinearRegression.

Parameters:

**params (dict) – Dictionary of parameters to update.

Returns:

The TimeWeightedLinearRegression instance with updated parameters.

Return type:

self

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

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