`sklearn.decomposition`.MiniBatchSparsePCA#

class sklearn.decomposition.MiniBatchSparsePCA(n_components=None, *, alpha=1, ridge_alpha=0.01, max_iter=1000, callback=None, batch_size=3, verbose=False, shuffle=True, n_jobs=None, method='lars', random_state=None, tol=0.001, max_no_improvement=10)[source]#

Mini-batch Sparse Principal Components Analysis.

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.

For an example comparing sparse PCA to PCA, see Faces dataset decompositions

See also

DictionaryLearning: Find a dictionary that sparsely encodes data.
IncrementalPCA: Incremental principal components analysis.
PCA: Principal component analysis.
SparsePCA: Sparse Principal Components Analysis.
TruncatedSVD: Dimensionality reduction using truncated SVD.

Examples

>>> import numpy as np
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.decomposition import MiniBatchSparsePCA
>>> X, _ = make_friedman1(n_samples=200, n_features=30, random_state=0)
>>> transformer = MiniBatchSparsePCA(n_components=5, batch_size=50,
...                                  max_iter=10, random_state=0)
>>> transformer.fit(X)
MiniBatchSparsePCA(...)
>>> X_transformed = transformer.transform(X)
>>> X_transformed.shape
(200, 5)
>>> # most values in the components_ are zero (sparsity)
>>> np.mean(transformer.components_ == 0)
0.9...

Methods

`fit`(X[, y])	Fit the model from data in X.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_feature_names_out`([input_features])	Get output feature names for transformation.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(X)	Transform data from the latent space to the original space.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Least Squares projection of the data onto the sparse components.

fit(X, y=None)[source]#

Fit the model from data in X.

Parameters:

Xarray-like of shape (n_samples, n_features): Training vector, where n_samples is the number of samples and n_features is the number of features.
yIgnored: Not used, present here for API consistency by convention.

Returns:

selfobject: Returns the instance itself.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: ["class_name0", "class_name1", "class_name2"].

Parameters:

input_featuresarray-like of str or None, default=None: Only used to validate feature names with the names seen in fit.

Returns:

feature_names_outndarray of str objects: Transformed feature names.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

inverse_transform(X)[source]#

Transform data from the latent space to the original space.

This inversion is an approximation due to the loss of information induced by the forward decomposition.

New in version 1.2.

Parameters:

Xndarray of shape (n_samples, n_components): Data in the latent space.

Returns:

X_originalndarray of shape (n_samples, n_features): Reconstructed data in the original space.

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

"default": Default output format of a transformer
"pandas": DataFrame output
"polars": Polars output
None: Transform configuration is unchanged

New in version 1.4: "polars" option was added.

Returns:

selfestimator instance: Estimator instance.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

transform(X)[source]#

Least Squares projection of the data onto the sparse components.

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:

Xndarray 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.

Returns:

X_newndarray of shape (n_samples, n_components): Transformed data.

Examples using `sklearn.decomposition.MiniBatchSparsePCA`#

Faces dataset decompositions

sklearn.decomposition.MiniBatchSparsePCA#

Examples using sklearn.decomposition.MiniBatchSparsePCA#

`sklearn.decomposition`.MiniBatchSparsePCA#

Examples using `sklearn.decomposition.MiniBatchSparsePCA`#