Class SparsePCAScikitsLearnNode

Sparse Principal Components Analysis (SparsePCA)

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

Finds the set of sparse components that can optimally reconstruct
the data.  The amount of sparseness is controllable by the coefficient
of the L1 penalty, given by the parameter alpha.

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

**Parameters**

n_components : int,
    Number of sparse atoms to extract.

alpha : float,
    Sparsity controlling parameter. Higher values lead to sparser
    components.

ridge_alpha : float,
    Amount of ridge shrinkage to apply in order to improve
    conditioning when calling the transform method.

max_iter : int,
    Maximum number of iterations to perform.

tol : float,
    Tolerance for the stopping condition.

method : {'lars', 'cd'}
    lars: uses the least angle regression method to solve the lasso problem
    (linear_model.lars_path)
    cd: uses the coordinate descent method to compute the
    Lasso solution (linear_model.Lasso). Lars will be faster if
    the estimated components are sparse.

n_jobs : int,
    Number of parallel jobs to run.

U_init : array of shape (n_samples, n_components),
    Initial values for the loadings for warm restart scenarios.

V_init : array of shape (n_components, n_features),
    Initial values for the components for warm restart scenarios.

verbose :

    - Degree of verbosity of the printed output.


random_state : int or RandomState
    Pseudo number generator state used for random sampling.

**Attributes**

``components_`` : array, [n_components, n_features]
    Sparse components extracted from the data.

``error_`` : array
    Vector of errors at each iteration.

``n_iter_`` : int
    Number of iterations run.

See also

PCA
MiniBatchSparsePCA
DictionaryLearning

Overrides: object.__init__

Least Squares projection of the data onto the sparse components.

This node has been automatically generated by wrapping the sklearn.decomposition.sparse_pca.SparsePCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

To avoid instability issues in case the system is under-determined, regularization can be applied (Ridge regression) via the ridge_alpha parameter.

Note that Sparse PCA components orthogonality is not enforced as in PCA hence one cannot use a simple linear projection.

Parameters

X: array of shape (n_samples, n_features)

Test data to be transformed, must have the same number of features as the data used to train the model.

ridge_alpha: float, default: 0.01

Amount of ridge shrinkage to apply in order to improve conditioning.

Returns

X_new array, shape (n_samples, n_components)

Transformed data.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit the model from data in X.

This node has been automatically generated by wrapping the sklearn.decomposition.sparse_pca.SparsePCA 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 vector, where n_samples in the number of samples and n_features is the number of features.

Returns

self : object

Returns the instance itself.

Overrides: Node.stop_training