Class MiniBatchSparsePCAScikitsLearnNode

Mini-batch Sparse Principal Components Analysis

This node has been automatically generated by wrapping the ``sklearn.decomposition.sparse_pca.MiniBatchSparsePCA`` 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 : int,
    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.

n_iter : int,
    number of iterations to perform for each mini batch

callback : callable,
    callable that gets invoked every five iterations

batch_size : int,
    the number of features to take in each mini batch

verbose :

    - degree of output the procedure will print


shuffle : boolean,
    whether to shuffle the data before splitting it in batches

n_jobs : int,
    number of parallel jobs to run, or -1 to autodetect.

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.

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
SparsePCA
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.MiniBatchSparsePCA 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.MiniBatchSparsePCA 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