celer.MultiTaskLasso#

class celer.MultiTaskLasso(alpha=1.0, max_iter=100, max_epochs=50000, p0=10, verbose=0, tol=0.0001, prune=True, fit_intercept=True, warm_start=False)[source]#

MultiTaskLasso scikit-learn estimator based on Celer solver

The optimization objective for MultiTaskLasso is:

(1 / (2 * n_samples)) * ||y - X W||^2_2 + alpha * ||W||_{21}
Parameters:
alphafloat, optional

Constant that multiplies the L1 term. Defaults to 1.0. alpha = 0 is equivalent to an ordinary least square. For numerical reasons, using alpha = 0 with the Lasso object is not advised.

max_iterint, optional

The maximum number of iterations (subproblem definitions)

max_epochsint

Maximum number of CD epochs on each subproblem.

p0int

First working set size.

verbosebool or integer

Amount of verbosity.

tolfloat, optional

Stopping criterion for the optimization: the solver runs until the duality gap is smaller than tol * norm(y) ** 2 / len(y) or the maximum number of iteration is reached.

prunebool, optional (default=True)

Whether or not to use pruning when growing working sets.

fit_interceptbool, optional (default=True)

Whether or not to fit an intercept.

warm_startbool, optional (default=False)

When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.

References

[1]

M. Massias, A. Gramfort, J. Salmon “Celer: a Fast Solver for the Lasso wit Dual Extrapolation”, ICML 2018, http://proceedings.mlr.press/v80/massias18a.html

Attributes:
coef_array, shape (n_features,)

parameter vector (w in the cost function formula)

sparse_coef_scipy.sparse matrix, shape (n_features, 1)

Sparse representation of the fitted coef_.

intercept_float

constant term in decision function.

n_iter_int

Number of subproblems solved by Celer to reach the specified tolerance.

__init__(alpha=1.0, max_iter=100, max_epochs=50000, p0=10, verbose=0, tol=0.0001, prune=True, fit_intercept=True, warm_start=False)[source]#

Methods

__init__([alpha, max_iter, max_epochs, p0, ...])

fit(X, y)

Fit MultiTaskLasso model with Celer

get_params([deep])

Get parameters for this estimator.

path(X, y, *[, l1_ratio, eps, n_alphas, ...])

Compute elastic net path with coordinate descent.

predict(X)

Predict using the linear model.

score(X, y[, sample_weight])

Return the coefficient of determination of the prediction.

set_params(**params)

Set the parameters of this estimator.

Attributes

sparse_coef_

Sparse representation of the fitted coef_.