sklearn.decomposition.SparseCoder

class sklearn.decomposition.SparseCoder(dictionary, *, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, split_sign=False, n_jobs=None, positive_code=False, transform_max_iter=1000)[source]

Sparse coding.

Finds a sparse representation of data against a fixed, precomputed dictionary.

Each row of the result is the solution to a sparse coding problem. The goal is to find a sparse array code such that:

X ~= code * dictionary

Read more in the User Guide.

Parameters
dictionaryndarray of shape (n_components, n_features)

The dictionary atoms used for sparse coding. Lines are assumed to be normalized to unit norm.

transform_algorithm{‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’}, default=’omp’

Algorithm used to transform the data:

  • 'lars': uses the least angle regression method (linear_model.lars_path);

  • 'lasso_lars': uses Lars to compute the Lasso solution;

  • 'lasso_cd': uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). 'lasso_lars' will be faster if the estimated components are sparse;

  • 'omp': uses orthogonal matching pursuit to estimate the sparse solution;

  • 'threshold': squashes to zero all coefficients less than alpha from the projection dictionary * X'.

transform_n_nonzero_coefsint, default=None

Number of nonzero coefficients to target in each column of the solution. This is only used by algorithm='lars' and algorithm='omp' and is overridden by alpha in the omp case. If None, then transform_n_nonzero_coefs=int(n_features / 10).

transform_alphafloat, default=None

If algorithm='lasso_lars' or algorithm='lasso_cd', alpha is the penalty applied to the L1 norm. If algorithm='threshold', alpha is the absolute value of the threshold below which coefficients will be squashed to zero. If algorithm='omp', alpha is the tolerance parameter: the value of the reconstruction error targeted. In this case, it overrides n_nonzero_coefs. If None, default to 1.

split_signbool, default=False

Whether to split the sparse feature vector into the concatenation of its negative part and its positive part. This can improve the performance of downstream classifiers.

n_jobsint, default=None

Number of parallel jobs to run. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

positive_codebool, default=False

Whether to enforce positivity when finding the code.

New in version 0.20.

transform_max_iterint, default=1000

Maximum number of iterations to perform if algorithm='lasso_cd' or lasso_lars.

New in version 0.22.

Attributes
components_ndarray of shape (n_components, n_features)

DEPRECATED: The attribute components_ is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26).

n_components_int

Number of atoms.

n_features_in_int

Number of features seen during fit.

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.

MiniBatchDictionaryLearning

A faster, less accurate, version of the dictionary learning algorithm.

MiniBatchSparsePCA

Mini-batch Sparse Principal Components Analysis.

SparsePCA

Mini-batch Sparse Principal Components Analysis.

sparse_encode

Sparse coding where each row of the result is the solution to a sparse coding problem.

Examples

>>> import numpy as np
>>> from sklearn.decomposition import SparseCoder
>>> X = np.array([[-1, -1, -1], [0, 0, 3]])
>>> dictionary = np.array(
...     [[0, 1, 0],
...      [-1, -1, 2],
...      [1, 1, 1],
...      [0, 1, 1],
...      [0, 2, 1]],
...    dtype=np.float64
... )
>>> coder = SparseCoder(
...     dictionary=dictionary, transform_algorithm='lasso_lars',
...     transform_alpha=1e-10,
... )
>>> coder.transform(X)
array([[ 0.,  0., -1.,  0.,  0.],
       [ 0.,  1.,  1.,  0.,  0.]])

Methods

fit(X[, y])

Do nothing and return the estimator unchanged.

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[, y])

Encode the data as a sparse combination of the dictionary atoms.
property components_

DEPRECATED: The attribute components_ is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Use the dictionary instead.

fit(X, y=None)[source]

Do nothing and return the estimator unchanged.

This method is just there to implement the usual API and hence work in pipelines.

Parameters
XIgnored

Not used, present for API consistency by convention.

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 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_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.

property n_components_

Number of atoms.

property n_features_in_

Number of features seen during fit.

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, y=None)[source]

Encode the data as a sparse combination of the dictionary atoms.

Coding method is determined by the object parameter transform_algorithm.

Parameters
Xndarray 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 for API consistency by convention.

Returns
X_newndarray of shape (n_samples, n_components)

Transformed data.

Examples using sklearn.decomposition.SparseCoder

Sparse coding with a precomputed dictionary

Sparse coding with a precomputed dictionary