celer.ElasticNetCV#

class celer.ElasticNetCV(l1_ratio=1.0, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, max_iter=100, tol=0.0001, cv=None, verbose=0, max_epochs=50000, p0=10, prune=True, precompute='auto', positive=False, n_jobs=None)[source]#

ElasticNetCV scikit-learn estimator based on Celer solver

The best model is selected by cross-validation.

The optimization objective for ElasticNet is:

1 / (2 * n_samples) * ||y - X w||^2_2
+ alpha * l1_ratio * \sum_j weights_j |w_j|
+ 0.5 * alpha * (1 - l1_ratio) * \sum_j weights_j |w_j|^2)

Parameters:

l1_ratiofloat or list of float, optional: The ElasticNet mixing parameter, with 0 < l1_ratio <= 1. Defaults to 1.0 which corresponds to L1 penalty (Lasso). l1_ratio = 0 (Ridge regression) is not supported. This parameter can be a list, in which case the different values are tested by cross-validation and the one giving the best prediction score is used. Note that a good choice of list of values for l1_ratio is often to put more values close to 1 (i.e. Lasso) and less close to 0 (i.e. Ridge), as in [.1, .5, .7, .9, .95, .99, 1].
epsfloat, optional: Length of the path. eps=1e-3 means that alpha_min / alpha_max = 1e-3.
n_alphasint, optional: Number of alphas along the regularization path.
alphasnumpy array, optional: List of alphas where to compute the models If None alphas are set automatically.
fit_interceptboolean, default True: whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
max_iterint, optional: The maximum number of iterations (subproblem definitions).
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.
cvint, cross-validation generator or an iterable, optional: Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - An object to be used as a cross-validation generator. - An iterable yielding train/test splits. For integer/None inputs, sklearn KFold is used.
verbosebool or integer: Amount of verbosity.
max_epochsint, optional (default=50000): Maximum number of coordinate descent epochs when solving a subproblem.
p0int, optional (default=10): Number of features in the first working set.
prunebool, optional (default=False): Whether to use pruning when growing the working sets.
precomputeignored parameter, kept for sklearn compatibility.
positivebool, optional (default=False): When set to True, forces the coefficients to be positive.
n_jobsint or None, optional (default=None): Number of CPUs to use during the cross validation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

See also

celer_path
Lasso

Attributes:

alpha_float: The amount of penalization chosen by cross validation.
l1_ratio_float: The compromise between l1 and l2 penalization chosen by cross validation.
coef_array, shape (n_features,): parameter vector (w in the cost function formula).
intercept_float: independent term in decision function.
mse_path_array, shape (n_alphas, n_folds): mean square error for the test set on each fold, varying alpha.
alphas_numpy array, shape (n_alphas,): The grid of alphas used for fitting.
dual_gap_ndarray, shape (): The dual gap at the end of the optimization for the optimal alpha (alpha_).
n_iter_int: number of iterations run by the coordinate descent solver to reach the specified tolerance for the optimal alpha.

__init__(l1_ratio=1.0, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, max_iter=100, tol=0.0001, cv=None, verbose=0, max_epochs=50000, p0=10, prune=True, precompute='auto', positive=False, n_jobs=None)[source]#

Methods

`__init__`([l1_ratio, eps, n_alphas, alphas, ...])
`fit`(X, y[, sample_weight])	Fit linear model with coordinate descent.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`path`(X, y, alphas[, coef_init])	Compute Lasso path with Celer.
`predict`(X)	Predict using the linear model.
`score`(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
`set_fit_request`(*[, sample_weight])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.