API Documentation

Estimators

ElasticNet([alpha, l1_ratio, max_iter, ...])	ElasticNet scikit-learn estimator based on Celer solver
ElasticNetCV([l1_ratio, eps, n_alphas, ...])	ElasticNetCV scikit-learn estimator based on Celer solver
GroupLasso([groups, alpha, max_iter, ...])	Group Lasso scikit-learn estimator based on Celer solver
GroupLassoCV([groups, eps, n_alphas, ...])	GroupLassoCV scikit-learn estimator based on Celer solver
Lasso([alpha, max_iter, max_epochs, p0, ...])	Lasso scikit-learn estimator based on Celer solver
LassoCV([eps, n_alphas, alphas, ...])	LassoCV scikit-learn estimator based on Celer solver
LogisticRegression([C, penalty, solver, ...])	Sparse Logistic regression scikit-learn estimator based on Celer solver.
MultiTaskLasso([alpha, max_iter, ...])	MultiTaskLasso scikit-learn estimator based on Celer solver
MultiTaskLassoCV([eps, n_alphas, alphas, ...])	MultiTaskLassoCV scikit-learn estimator based on Celer solver

Functions

celer_path(X, y, pb[, eps, n_alphas, ...])

Compute optimization path with Celer as inner solver.

Datasets fetchers

celer.datasets:

make_correlated_data([n_samples, ...])	Generate correlated design matrix with decaying correlation rho**|i-j|.
fetch_ml_uci(dataset)	Get a datasest from ML UCI database.
fetch_libsvm(dataset[, replace, normalize, ...])	This function is deprecated, we now rely on the libsvmdata package.
fetch_climate([replace])	Get design matrix and observation for the climate dataset.