macrosynergy.learning.forecasting.linear_model.lad_regressors.lad_regressor#

class LADRegressor(fit_intercept=True, positive=False, alpha=0, shrinkage_type='l1', tol=None, maxiter=None)[source]#

Bases: BaseEstimator, RegressorMixin

fit(X, y, sample_weight=None)[source]#

Learn LAD regression model parameters.

Parameters:

  • X (pd.DataFrame or np.ndarray) – Input feature matrix.

  • y (pd.Series or pd.DataFrame or np.ndarray) – Target vector associated with each sample in X.

  • sample_weight (np.ndarray, default=None) – Numpy array of sample weights to create a weighted LAD regression model.

predict(X)[source]#

Predict dependent variable using the fitted LAD regression model.

Parameters:

X (pd.DataFrame or np.ndarray) – Input feature matrix.

Returns:

y_pred – Numpy array of predictions.

Return type:

np.ndarray

Notes

If the model learning algorithm failed to converge, the predict method will return an array of zeros. This has the interpretation of no buy/sell signal being triggered based on this model.

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

Configure whether metadata should be requested to be passed to the fit 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 fit 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 fit.

  • 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 fit.

Returns:

self – The updated object.

Return type:

object

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

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