Class LassoLarsScikitsLearnNode

Lasso model fit with Least Angle Regression a.k.a. Lars

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

It is a Linear Model trained with an L1 prior as regularizer.

The optimization objective for Lasso is:

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

Parameters

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).

verbose : boolean or integer, optional

Sets the verbosity amount

normalize : boolean, optional

If True, the regressors X are normalized

copy_X : boolean, optional, default True

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

precompute : True | False | 'auto' | array-like

Whether to use a precomputed Gram matrix to speed up calculations. If set to 'auto' let us decide. The Gram matrix can also be passed as argument.

max_iter: integer, optional

Maximum number of iterations to perform.

eps: float, optional

The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the 'tol' parameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization.

fit_path : boolean

If True the full path is stored in the coef_path_ attribute. If you compute the solution for a large problem or many targets, setting fit_path to False will lead to a speedup, especially with a small alpha.

Attributes

coef_path_ : array, shape = [n_features, n_alpha]

The varying values of the coefficients along the path. It is not present if fit_path parameter is False.

coef_ : array, shape = [n_features]

Parameter vector (w in the fomulation formula).

intercept_ : float

Independent term in decision function.

Examples

>>> from sklearn import linear_model
>>> clf = linear_model.LassoLars(alpha=0.01)
>>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1, 0, -1])
... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
LassoLars(alpha=0.01, copy_X=True, eps=..., fit_intercept=True,
     fit_path=True, max_iter=500, normalize=True, precompute='auto',
     verbose=False)
>>> print(clf.coef_) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
[ 0.         -0.963257...]

See also

lars_path lasso_path Lasso LassoCV LassoLarsCV sklearn.decomposition.sparse_encode

http://en.wikipedia.org/wiki/Least_angle_regression

Overrides: object.__init__

Predict using the linear model

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

Parameters

X : numpy array of shape [n_samples, n_features]

Returns

C : array, shape = [n_samples]

Returns predicted values.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit the model using X, y as training data.

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

parameters

X : array-like, shape = [n_samples, n_features]

training data.

y : array-like, shape = [n_samples] or [n_samples, n_targets]

target values.

Xy : array-like, shape = [n_samples] or [n_samples, n_targets], optional

Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed.

returns

self : object

returns an instance of self.

Overrides: Node.stop_training