mojolearn.MiniBatchDictionaryLearning#

class mojolearn.MiniBatchDictionaryLearning(n_components=None, *, alpha=1, max_iter=1000, fit_algorithm='lars', n_jobs=None, batch_size=256, shuffle=True, dict_init=None, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, verbose=False, split_sign=False, random_state=None, positive_code=False, positive_dict=False, transform_max_iter=1000, callback=None, tol=0.001, max_no_improvement=10, numeric_mode=None)[source]#

Bases: _SparseCoding

Learn a sparse-coding dictionary from mini-batches.

Follows scikit-learn’s MiniBatchDictionaryLearning. The starting dictionary comes from an exact SVD (scikit-learn uses a randomized one) and the shuffle uses mojolearn’s own generator, so results differ slightly from scikit-learn’s for the same seed.

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 passes over the data.

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

  • n_jobs (ignored)

  • batch_size (int, default 256) – Rows per mini-batch (capped at n_samples).

  • shuffle (bool, default True) – Shuffle the rows once before fitting.

  • dict_init (array-like of shape (n_components, n_features) or None, default None) – Starting dictionary.

  • 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.

  • 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 for the shuffle; 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.

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

  • tol (float, default 1e-3) – Stop when the dictionary changes less than this between steps.

  • max_no_improvement (int or None, default 10) – Stop after this many mini-batches without improvement of the smoothed cost; None disables this test.

  • numeric_mode ({“identical”, “fast”} or None, default None) – Numeric mode for this estimator. None uses the package default ("identical" unless changed). "identical" gives the same bits on NVIDIA and AMD GPUs; "fast" applies on Apple GPUs.

Variables:

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

  • n_iter_ (int) – Passes over the data.

  • n_steps_ (int) – Mini-batches processed.

  • n_features_in_ (int)

Examples

>>> import numpy as np, mojolearn
>>> X = np.random.default_rng(0).random((1000, 8), dtype=np.float32)
>>> dl = mojolearn.MiniBatchDictionaryLearning(n_components=5, max_iter=5, random_state=0).fit(X)
>>> code = dl.transform(X)
__init__(n_components=None, *, alpha=1, max_iter=1000, fit_algorithm='lars', n_jobs=None, batch_size=256, shuffle=True, dict_init=None, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None, verbose=False, split_sign=False, random_state=None, positive_code=False, positive_dict=False, transform_max_iter=1000, callback=None, tol=0.001, max_no_improvement=10, numeric_mode=None)[source]#

Methods

fit(X[, y])

Fit the dictionary to X.

fit_transform(X[, y])

Fit the dictionary, then return transform(X).

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)[source]#

Fit the dictionary to X.

Parameters:

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

  • y (ignored)

Returns:

self

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

Fit the dictionary, then return transform(X).

Parameters:

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

  • y (ignored)

Returns:

ndarray of shape (n_samples, n_components), float32