sklearn.decomposition
.MiniBatchSparsePCA¶
- class sklearn.decomposition.MiniBatchSparsePCA(n_components=None, *, alpha=1, ridge_alpha=0.01, n_iter=100, callback=None, batch_size=3, verbose=False, shuffle=True, n_jobs=None, method='lars', random_state=None)[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.
Read more in the User Guide.
- Parameters
- n_componentsint, default=None
Number of sparse atoms to extract.
- alphaint, default=1
Sparsity controlling parameter. Higher values lead to sparser components.
- ridge_alphafloat, default=0.01
Amount of ridge shrinkage to apply in order to improve conditioning when calling the transform method.
- n_iterint, default=100
Number of iterations to perform for each mini batch.
- callbackcallable, default=None
Callable that gets invoked every five iterations.
- batch_sizeint, default=3
The number of features to take in each mini batch.
- verboseint or bool, default=False
Controls the verbosity; the higher, the more messages. Defaults to 0.
- shufflebool, default=True
Whether to shuffle the data before splitting it in batches.
- n_jobsint, default=None
Number of parallel jobs to run.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details.- method{‘lars’, ‘cd’}, default=’lars’
Method to be used for optimization. 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_stateint, RandomState instance or None, default=None
Used for random shuffling when
shuffle
is set toTrue
, during online dictionary learning. Pass an int for reproducible results across multiple function calls. See Glossary.
- Attributes
- components_ndarray of shape (n_components, n_features)
Sparse components extracted from the data.
- n_components_int
Estimated number of components.
New in version 0.23.
- n_iter_int
Number of iterations run.
- mean_ndarray of shape (n_features,)
Per-feature empirical mean, estimated from the training set. Equal to
X.mean(axis=0)
.- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
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, ... 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) 0.94
Methods
fit
(X[, y])Fit the model from data in X.
fit_transform
(X[, y])Fit to data, then transform it.
get_params
([deep])Get parameters for this estimator.
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 andn_features
is the number of features.- yIgnored
Not used, present 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
andy
with optional parametersfit_params
and returns a transformed version ofX
.- 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_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.
- 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.