Class MultiTaskElasticNetCVScikitsLearnNode

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

This node has been automatically generated by wrapping the ``sklearn.linear_model.coordinate_descent.MultiTaskElasticNetCV`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

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 :ref:`User Guide <multi_task_lasso>`.

**Parameters**

eps : float, optional
    Length of the path. ``eps=1e-3`` means that
    ``alpha_min / alpha_max = 1e-3``.

alphas : array-like, optional
    List of alphas where to compute the models.
    If not provided, set automatically.

n_alphas : int, optional
    Number of alphas along the regularization path

l1_ratio : float or array of floats
    The ElasticNet mixing parameter, with 0 < l1_ratio <= 1.
    For l1_ratio = 0 the penalty is an L1/L2 penalty. For l1_ratio = 1 it
    is an L1 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]``

fit_intercept : boolean
    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).

normalize : boolean, optional, default False
    If ``True``, the regressors X will be normalized before regression.

copy_X : boolean, optional, default True
    If ``True``, X will be copied; else, it may be overwritten.

max_iter : int, optional
    The maximum number of iterations

tol : float, optional
    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``.

cv : int, 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, :class:`KFold` is used.

    Refer :ref:`User Guide <cross_validation>` for the various
    cross-validation strategies that can be used here.

verbose : bool or integer
    Amount of verbosity.

n_jobs : integer, optional
    Number of CPUs to use during the cross validation. If ``-1``, use
    all the CPUs. Note that this is used only if multiple values for
    l1_ratio are given.

selection : str, 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.

random_state : int, RandomState instance, or None (default)
    The seed of the pseudo random number generator that selects
    a random feature to update. Useful only when selection is set to
    'random'.

**Attributes**

``intercept_`` : array, shape (n_tasks,)
    Independent term in decision function.

``coef_`` : array, shape (n_tasks, n_features)
    Parameter vector (W in the cost function formula).

``alpha_`` : float
    The amount of penalization chosen by cross validation

``mse_path_`` : array, 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_`` : numpy array, 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.

**Examples**

>>> from sklearn import linear_model
>>> clf = linear_model.MultiTaskElasticNetCV()
>>> clf.fit([[0,0], [1, 1], [2, 2]],
...         [[0, 0], [1, 1], [2, 2]])
... #doctest: +NORMALIZE_WHITESPACE
MultiTaskElasticNetCV(alphas=None, copy_X=True, cv=None, eps=0.001,
       fit_intercept=True, l1_ratio=0.5, max_iter=1000, n_alphas=100,
       n_jobs=1, normalize=False, random_state=None, selection='cyclic',
       tol=0.0001, verbose=0)
>>> print(clf.coef_)
[[ 0.52875032  0.46958558]
 [ 0.52875032  0.46958558]]
>>> print(clf.intercept_)
[ 0.00166409  0.00166409]

See also

MultiTaskElasticNet
ElasticNetCV
MultiTaskLassoCV

**Notes**

The algorithm used to fit the model is coordinate descent.

To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.

Overrides: object.__init__

Predict using the linear model

This node has been automatically generated by wrapping the sklearn.linear_model.coordinate_descent.MultiTaskElasticNetCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape = (n_samples, n_features)

Samples.

Returns

C : array, shape = (n_samples,)

Returns predicted values.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit linear model with coordinate descent

This node has been automatically generated by wrapping the sklearn.linear_model.coordinate_descent.MultiTaskElasticNetCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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

Parameters

X : {array-like}, shape (n_samples, n_features)

Training data. Pass directly as float64, Fortran-contiguous data to avoid unnecessary memory duplication. If y is mono-output, X can be sparse.

y : array-like, shape (n_samples,) or (n_samples, n_targets)

Target values

Overrides: Node.stop_training