`sklearn.linear_model`.MultiTaskElasticNetCV¶

class sklearn.linear_model.MultiTaskElasticNetCV(*, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize='deprecated', max_iter=1000, tol=0.0001, cv=None, copy_X=True, verbose=0, n_jobs=None, random_state=None, selection='cyclic')[source]¶

Multi-task L1/L2 ElasticNet with built-in cross-validation.

See glossary entry for cross-validation estimator.

The optimization objective for MultiTaskElasticNet is:

(1 / (2 * n_samples)) * ||Y - XW||^Fro_2
+ alpha * l1_ratio * ||W||_21
+ 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2

Where:

||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}

i.e. the sum of norm of each row.

Read more in the User Guide.

New in version 0.15.

Parameters:

l1_ratiofloat or list of float, default=0.5

The ElasticNet mixing parameter, with 0 < l1_ratio <= 1. For l1_ratio = 1 the penalty is an L1/L2 penalty. For l1_ratio = 0 it is an L2 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1/L2 and L2. 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, default=1e-3

Length of the path. eps=1e-3 means that alpha_min / alpha_max = 1e-3.

n_alphasint, default=100

Number of alphas along the regularization path.

alphasarray-like, default=None

List of alphas where to compute the models. If not provided, set automatically.

fit_interceptbool, default=True

Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).

normalizebool, default=False

This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.

Deprecated since version 1.0: normalize was deprecated in version 1.0 and will be removed in 1.2.

max_iterint, default=1000

The maximum number of iterations.

tolfloat, default=1e-4

The tolerance for the optimization: if the updates are smaller than tol, the optimization code checks the dual gap for optimality and continues until it is smaller than tol.

cvint, cross-validation generator or iterable, default=None

Determines the cross-validation splitting strategy. Possible inputs for cv are:

None, to use the default 5-fold cross-validation,
int, to specify the number of folds.
CV splitter,
An iterable yielding (train, test) splits as arrays of indices.

For int/None inputs, KFold is used.

Refer User Guide for the various cross-validation strategies that can be used here.

Changed in version 0.22: cv default value if None changed from 3-fold to 5-fold.

copy_Xbool, default=True

If True, X will be copied; else, it may be overwritten.

verbosebool or int, default=0

Amount of verbosity.

n_jobsint, default=None

Number of CPUs to use during the cross validation. Note that this is used only if multiple values for l1_ratio are given. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

random_stateint, RandomState instance, default=None

The seed of the pseudo random number generator that selects a random feature to update. Used when selection == ‘random’. Pass an int for reproducible output across multiple function calls. See Glossary.

selection{‘cyclic’, ‘random’}, default=’cyclic’

If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e-4.

Attributes:

intercept_ndarray of shape (n_targets,): Independent term in decision function.
coef_ndarray of shape (n_targets, n_features): Parameter vector (W in the cost function formula). Note that coef_ stores the transpose of W, W.T.
alpha_float: The amount of penalization chosen by cross validation.
mse_path_ndarray of shape (n_alphas, n_folds) or (n_l1_ratio, n_alphas, n_folds): Mean square error for the test set on each fold, varying alpha.
alphas_ndarray of shape (n_alphas,) or (n_l1_ratio, n_alphas): The grid of alphas used for fitting, for each l1_ratio.
l1_ratio_float: Best l1_ratio obtained by cross-validation.
n_iter_int: Number of iterations run by the coordinate descent solver to reach the specified tolerance for the optimal alpha.
dual_gap_float: The dual gap at the end of the optimization for the optimal alpha.
n_features_in_int: Number of features seen during fit.

New in version 0.24.
feature_names_in_ndarray of shape (n_features_in_,): Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

See also

MultiTaskElasticNet: Multi-task L1/L2 ElasticNet with built-in cross-validation.
ElasticNetCV: Elastic net model with best model selection by cross-validation.
MultiTaskLassoCV: Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.

Notes

The algorithm used to fit the model is coordinate descent.

In fit, once the best parameters l1_ratio and alpha are found through cross-validation, the model is fit again using the entire training set.

To avoid unnecessary memory duplication the X and y arguments of the fit method should be directly passed as Fortran-contiguous numpy arrays.

Examples

>>> from sklearn import linear_model
>>> clf = linear_model.MultiTaskElasticNetCV(cv=3)
>>> clf.fit([[0,0], [1, 1], [2, 2]],
...         [[0, 0], [1, 1], [2, 2]])
MultiTaskElasticNetCV(cv=3)
>>> print(clf.coef_)
[[0.52875032 0.46958558]
 [0.52875032 0.46958558]]
>>> print(clf.intercept_)
[0.00166409 0.00166409]

Methods

`fit`(X, y)	Fit MultiTaskElasticNet model with coordinate descent.
`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.

fit(X, y)[source]¶

Fit MultiTaskElasticNet model with coordinate descent.

Fit is on grid of alphas and best alpha estimated by cross-validation.

Parameters:

Xndarray of shape (n_samples, n_features): Training data.
yndarray of shape (n_samples, n_targets): Training target variable. Will be cast to X’s dtype if necessary.

Returns:

selfobject: Returns MultiTaskElasticNet instance.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

static path(X, y, *, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, precompute='auto', Xy=None, copy_X=True, coef_init=None, verbose=False, return_n_iter=False, positive=False, check_input=True, **params)[source]¶

Compute elastic net path with coordinate descent.

The elastic net optimization function varies for mono and multi-outputs.

For mono-output tasks it is:

1 / (2 * n_samples) * ||y - Xw||^2_2
+ alpha * l1_ratio * ||w||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2

For multi-output tasks it is:

(1 / (2 * n_samples)) * ||Y - XW||_Fro^2
+ alpha * l1_ratio * ||W||_21
+ 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2

Where:

||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}

i.e. the sum of norm of each row.

sklearn.linear_model.MultiTaskElasticNetCV¶

`sklearn.linear_model`.MultiTaskElasticNetCV¶