el0ps.estimator.L0L1L2Regressor

class el0ps.estimator.L0L1L2Regressor(lmbd, alpha, beta, M=inf, fit_intercept=False, solver=<el0ps.solver.bnb.BnbSolver object>)

Scikit-learn-compatible linear model regression estimators with L0L1L2-regularization.

The estimator corresponds to a solution of the problem

\[\textstyle\min_{\|\mathbf{x}\|_{\infty} \leq M} f(\mathbf{Ax}) + \lambda\|\mathbf{x}\|_0 + \alpha\|\mathbf{x}\|_1 + \beta\|\mathbf{x}\|_2^2\]

where \(f\) is a el0ps.datafit.Leastsquares function, \(\mathbf{A} \in \mathbb{R}^{m \times n}\) is a matrix, \(\lambda > 0\) is a parameter, the L0-norm \(\|\cdot\|_0\) counts the number of non-zero entries in its input, and \(h\) is a penalty function.

__init__(lmbd, alpha, beta, M=inf, fit_intercept=False, solver=<el0ps.solver.bnb.BnbSolver object>)

Methods

__init__(lmbd, alpha, beta[, M, ...])
fit(X, y)	Fit model.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
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.
set_score_request(*[, sample_weight])	Request metadata passed to the score method.