mojolearn.DictionaryLearning#

class mojolearn.DictionaryLearning(n_components=None, *, alpha=1, max_iter=1000, tol=1e-08, fit_algorithm='lars', transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, n_jobs=None, code_init=None, dict_init=None, callback=None, verbose=False, split_sign=False, random_state=None, positive_code=False, positive_dict=False, transform_max_iter=1000, numeric_mode=None)#

Learn a dictionary in which the data has a sparse representation.

Follows scikit-learn’s DictionaryLearning: alternates sparse coding of X with updates of the dictionary atoms. The starting dictionary comes from an exact SVD of X, and both fit_algorithm values solve the same Lasso problem by coordinate descent.

Parameters:

  • n_components (int or None, default None) – Number of atoms; None uses n_features.

  • alpha (float, default 1) – Sparsity penalty used while fitting.

  • max_iter (int, default 1000) – Maximum number of iterations.

  • tol (float, default 1e-8) – Convergence tolerance.

  • fit_algorithm ({“lars”, “cd”}, default “lars”) – Lasso solver name used while fitting (both give the same optimum).

  • transform_algorithm ({“omp”, “lasso_lars”, “lasso_cd”, “lars”, “threshold”}, default “omp”) – Method used by transform; see sparse_encode().

  • transform_n_nonzero_coefs (int or None, default None) – Nonzero coefficients for "omp" and "lars" in transform.

  • transform_alpha (float or None, default None) – Penalty or threshold used by transform; None uses alpha.

  • n_jobs (ignored)

  • code_init (array-like of shape (n_samples, n_components) or None, default None) – Starting code; used only together with dict_init.

  • dict_init (array-like of shape (n_components, n_features) or None, default None) – Starting dictionary; used only together with code_init.

  • callback (None) – Must be None; a callable raises NotImplementedError.

  • verbose (ignored)

  • split_sign (bool, default False) – Return the positive and negative parts of the code as separate columns.

  • random_state (int or None, default None) – Seed used to resample unused atoms; None means 0.

  • positive_code (bool, default False) – Force the code to be non-negative.

  • positive_dict (bool, default False) – Force the dictionary to be non-negative.

  • transform_max_iter (int, default 1000) – Maximum iterations of the Lasso methods during fit and transform.

  • numeric_mode ({“identical”, “fast”} or None, default None) – Numeric mode for this estimator. None uses the package default ("identical" unless changed). In identical mode, mojolearn returns the same bits on Apple, NVIDIA and AMD GPUs. "fast" is available on Apple GPUs only.

Variables:

  • components_ (ndarray of shape (n_components, n_features)) – The dictionary atoms.

  • error_ (list of float) – Objective value at each iteration.

  • n_iter_ (int)

  • n_features_in_ (int)

Raises:

ValueError – For an unknown fit_algorithm or transform_algorithm.

Examples

>>> import numpy as np, mojolearn
>>> X = np.random.default_rng(0).random((500, 8), dtype=np.float32)
>>> dl = mojolearn.DictionaryLearning(n_components=5, max_iter=20, random_state=0)
>>> code = dl.fit_transform(X)

See also

User guide: Decomposition and embeddings

Choosing an estimator

Methods

fit(X[, y])

Fit the dictionary to X.

fit_transform(X[, y])

Fit the dictionary and return the code found while fitting.

get_params([deep])

Return this estimator's constructor parameters.

inverse_transform(X)

Rebuild data from a code: code @ components_.

set_params(**params)

Set constructor parameters by keyword.

transform(X)

Encode X as a sparse combination of the dictionary atoms.
fit(X, y=None)#

Fit the dictionary to X.

Parameters:

  • X (array-like of shape (n_samples, n_features))

  • y (ignored)

Returns:

self

fit_transform(X, y=None)#

Fit the dictionary and return the code found while fitting.

Parameters:

  • X (array-like of shape (n_samples, n_features))

  • y (ignored)

Returns:

ndarray of shape (n_samples, n_components), float32 – Twice as many columns when split_sign=True.

get_params(deep=True)#

Return this estimator’s constructor parameters.

Parameters:

deep (bool, default True) – Accepted for scikit-learn compatibility; it has no effect.

Returns:

dict – Parameter name to current value.

inverse_transform(X)#

Rebuild data from a code: code @ components_.

Parameters:

X (array-like of shape (n_samples, n_components)) – A code, as returned by transform (with 2 * n_components columns when split_sign=True).

Returns:

ndarray of shape (n_samples, n_features), float32

set_params(**params)#

Set constructor parameters by keyword.

Raises:

ValueError – If a keyword is not a parameter of this estimator.

Returns:

self

transform(X)#

Encode X as a sparse combination of the dictionary atoms.

Uses transform_algorithm and its settings.

Parameters:

X (array-like of shape (n_samples, n_features))

Returns:

ndarray of shape (n_samples, n_components), float32 – Twice as many columns when split_sign=True.