Class MultiTaskLassoScikitsLearnNode

Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer

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

The optimization objective for Lasso is::


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

Where::


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

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

Read more in the :ref:`User Guide <multi_task_lasso>`.

**Parameters**

alpha : float, optional
    Constant that multiplies the L1/L2 term. Defaults to 1.0

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``.

warm_start : bool, optional
    When set to ``True``, reuse the solution of the previous call to fit as
    initialization, otherwise, just erase the previous solution.

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**

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

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

``n_iter_`` : int
    number of iterations run by the coordinate descent solver to reach
    the specified tolerance.

**Examples**

>>> from sklearn import linear_model
>>> clf = linear_model.MultiTaskLasso(alpha=0.1)
>>> clf.fit([[0,0], [1, 1], [2, 2]], [[0, 0], [1, 1], [2, 2]])
MultiTaskLasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000,
        normalize=False, random_state=None, selection='cyclic', tol=0.0001,
        warm_start=False)
>>> print(clf.coef_)
[[ 0.89393398  0.        ]
 [ 0.89393398  0.        ]]
>>> print(clf.intercept_)
[ 0.10606602  0.10606602]

See also

Lasso, MultiTaskElasticNet

**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.MultiTaskLasso 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 MultiTaskLasso model with coordinate descent

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

Parameters

X : ndarray, shape (n_samples, n_features)

Data

y : ndarray, shape (n_samples, n_tasks)

Target

Notes

Coordinate descent is an algorithm that considers each column of data at a time hence it will automatically convert the X input as a Fortran-contiguous numpy array if necessary.

To avoid memory re-allocation it is advised to allocate the initial data in memory directly using that format.

Overrides: Node.stop_training