collasso.multi_task module#

Multi-Task Learning.

Main Class:: CoopLassoCV - cross-validated cooperative multi-task lasso regression

Examples#

>>> from sklearn.datasets import load_linnerud
>>> from collasso import CoopLassoCV
>>> x, y = load_linnerud(return_X_y=True)
>>> model = CoopLassoCV()
>>> model.fit(x, y) # n_samples x p_features, n_samples x q_targets
>>> model.coef_ # q_targets x p_features
>>> y_pred = model.predict(x) # n_samples x q_targets

class collasso.multi_task.CoopLassoCV(*, cv: int = 5, n_alphas: int = 100, l1_ratio: float = 0.5, exp_y: float = 1, exp_x: float = 1, random_state: int | None = None)#

Bases: RegressorMixin, BaseEstimator

Cross-Validated Cooperative Multi-Task Lasso Regression.

Implements cooperative multi-task lasso regression, optimising the regularisation parameters by cross-validation.

Parameters:

cvint, default=5: Number of cross-validation folds.
n_alphasint, default=100: Number of candidate values for the regularisation parameter in the final regressions.
l1_ratiofloat, default=0.5: Elastic net mixing parameter for the initial regressions, with 0<=l1_ratio<=1, where l1_ratio=0 leads to L2 (ridge) and l1_ratio=1 leads to L1 (lasso) penalisation.
exp_yfloat, default=1.0: Non-negative number for exponentiating the target-target correlation coefficients.
exp_xfloat, default=1.0: Non-negative number for exponentiating the feature-feature correlation coefficients.
random_stateint or None, default=None: Random seed for generating reproducible cross-validation folds.

Attributes:

n_int: Number of training samples.
p_int: Number of features.
q_int: Number of targets.
model_list of length q_targets: Fitted models from _CoopLasso.
alpha_list length q_targets of ndarrays: Sequence of regularisation parameters.
mse_list of length q_targets of ndarrays: Cross-validated mean squared errors for each value of alpha.
min_list of length q_targets of ndarrays: Indices of regularisation parameters corresponding to the lowest mean squared error.
coef_ndarray of shape (q_targets, p_features): Estimated effects (of the feature in the column on the target in the row).

See also

IndepLassoCV: A convenience class using the same interface as CoopLassoCV (similarly formatted inputs and outputs) without sharing information among targets or features.
_CoopLasso: An internal class without cross-validation returning the lasso solution path. This is repeatedly called by CoopLassoCV (once in each cross-validation iteration and once for the full dataset).

Examples

>>> from sklearn.datasets import load_linnerud
>>> from collasso import CoopLassoCV
>>> x, y = load_linnerud(return_X_y=True)
>>> model = CoopLassoCV()
>>> model.fit(x, y) # n_samples x p_features, n_samples x q_targets
>>> model.coef_ # q_targets x p_features
>>> y_pred = model.predict(x) # n_samples x q_targets

fit(X: ndarray, y: ndarray, Z: ndarray | None = None) → CoopLassoCV#

Fit cross-validated model.

Fits cross-validated cooperative multi-task lasso regression.

Parameters:

Xndarray of shape (n_samples, p_features) or (n_samples, p_features, q_targets): Common feature matrix for all targets or a specific feature matrix for each target.
yndarray of shape (n_samples, q_targets): Target matrix.
Zndarray of shape (p_features,) or (p_features, q_targets), or None: Logical vector or matrix indicating primary (1, True) and auxiliary features (0, False) for all targets or each target.

Returns:

selfCoopLassoCV: Fitted model.

predict(X: ndarray) → ndarray#

Make predictions.

Make predictions from a cross-validated model obtained by cooperative multi-task lasso regression.

Parameters:

Xndarray of shape (n_samples, p_features) or (n_samples, p_features, q_targets): Common feature matrix for all targets, or a specific feature matrix for each target.

Returns:

y_hatndarray of shape (n_samples, q_targets): Matrix of predicted values (of the target in the column for the sample in the row).

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

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.

Added in version 1.3.

Parameters:

Zstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for Z parameter in fit.

Returns:

selfobject: The updated object.

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

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

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.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.