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.
Read more in the User Guide.
- Parameters:
- n_componentsint, default=None
Number of sparse atoms to extract. If None, then
n_components
is set ton_features
.- 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.
- max_iterint, default=1_000
Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics.
New in version 1.2.
Deprecated since version 1.4:
max_iter=None
is deprecated in 1.4 and will be removed in 1.6. Use the default value (i.e.100
) instead.- 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.- tolfloat, default=1e-3
Control early stopping based on the norm of the differences in the dictionary between 2 steps.
To disable early stopping based on changes in the dictionary, set
tol
to 0.0.New in version 1.1.
- max_no_improvementint or None, default=10
Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed cost function.
To disable convergence detection based on cost function, set
max_no_improvement
toNone
.New in version 1.1.
- 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, ... 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 of this object.
get_params
([deep])Get parameters for this estimator.
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 andn_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
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_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
andfit_transform
."default"
: Default output format of a transformer"pandas"
: DataFrame output"polars"
: Polars outputNone
: 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.