macrosynergy.learning.forecasting.linear_model.lad_regressors

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

Bases: BaseEstimator, RegressorMixin

fit(X, y, sample_weight=None)

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)

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

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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 SignWeightedLADRegressor(fit_intercept=True, positive=False, alpha=0, shrinkage_type='l1', tol=None, maxiter=None)

Bases: SignWeightedRegressor

set_params(**params)

Setter method to update the parameters of the SignWeightedLADRegressor.

Parameters:

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

Returns:

The SignWeightedLADRegressor instance with updated parameters.

Return type:

self

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

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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 TimeWeightedLADRegressor(fit_intercept=True, positive=False, half_life=252, alpha=0, shrinkage_type='l1', tol=None, maxiter=None)

Bases: TimeWeightedRegressor

set_params(**params)

Setter method to update the parameters of the TimeWeightedLADRegressor.

Parameters:

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

Returns:

The TimeWeightedLADRegressor instance with updated parameters.

Return type:

self

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

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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

• macrosynergy.learning.forecasting.linear_model.lad_regressors.lad_regressor
  • LADRegressor
    • LADRegressor.fit()
    • LADRegressor.predict()
    • LADRegressor.set_fit_request()
    • LADRegressor.set_score_request()
• macrosynergy.learning.forecasting.linear_model.lad_regressors.weighted_lad_regressors
  • SignWeightedLADRegressor
    • SignWeightedLADRegressor.set_params()
    • SignWeightedLADRegressor.set_score_request()
  • TimeWeightedLADRegressor
    • TimeWeightedLADRegressor.set_params()
    • TimeWeightedLADRegressor.set_score_request()