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.