sklearn.decomposition
.MiniBatchDictionaryLearning¶

class
sklearn.decomposition.
MiniBatchDictionaryLearning
(n_components=None, *, alpha=1, n_iter=1000, fit_algorithm='lars', n_jobs=None, batch_size=3, 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)[source]¶ Minibatch dictionary learning
Finds a dictionary (a set of atoms) that can best be used to represent data using a sparse code.
Solves the optimization problem:
(U^*,V^*) = argmin 0.5  X  U V _2^2 + alpha *  U _1 (U,V) with  V_k _2 = 1 for all 0 <= k < n_components
Read more in the User Guide.
 Parameters
 n_componentsint, default=None
number of dictionary elements to extract
 alphafloat, default=1
sparsity controlling parameter
 n_iterint, default=1000
total number of iterations to perform
 fit_algorithm{‘lars’, ‘cd’}, default=’lars’
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.
 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. batch_sizeint, default=3
number of samples in each minibatch
 shufflebool, default=True
whether to shuffle the samples before forming batches
 dict_initnbarray of shape (n_components, n_features), default=None
initial value of the dictionary for warm restart scenarios
 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,
0.1 * n_features
by default Number of nonzero coefficients to target in each column of the solution. This is only used by
algorithm='lars'
andalgorithm='omp'
and is overridden byalpha
in theomp
case. transform_alphafloat, 1. by default
If
algorithm='lasso_lars'
oralgorithm='lasso_cd'
,alpha
is the penalty applied to the L1 norm. Ifalgorithm='threshold'
,alpha
is the absolute value of the threshold below which coefficients will be squashed to zero. Ifalgorithm='omp'
,alpha
is the tolerance parameter: the value of the reconstruction error targeted. In this case, it overridesn_nonzero_coefs
. verbosebool, default=False
To control the verbosity of the procedure.
 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.
 random_stateint or RandomState instance, default=None
Used for initializing the dictionary when
dict_init
is not specified, randomly shuffling the data whenshuffle
is set toTrue
, and updating the dictionary. Pass an int for reproducible results across multiple function calls. See Glossary. positive_codebool, default=False
Whether to enforce positivity when finding the code.
New in version 0.20.
 positive_dictbool, default=False
Whether to enforce positivity when finding the dictionary.
New in version 0.20.
 transform_max_iterint, default=1000
Maximum number of iterations to perform if
algorithm='lasso_cd'
orlasso_lars
.New in version 0.22.
 Attributes
 components_ndarray of shape (n_components, n_features)
components extracted from the data
 inner_stats_tuple of (A, B) ndarrays
Internal sufficient statistics that are kept by the algorithm. Keeping them is useful in online settings, to avoid losing the history of the evolution, but they shouldn’t have any use for the end user. A (n_components, n_components) is the dictionary covariance matrix. B (n_features, n_components) is the data approximation matrix
 n_iter_int
Number of iterations run.
 iter_offset_int
The number of iteration on data batches that has been performed before.
 random_state_RandomState
RandomState instance that is generated either from a seed, the random number generattor or by
np.random
.
Notes
References:
J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009: Online dictionary learning for sparse coding (https://www.di.ens.fr/sierra/pdfs/icml09.pdf)
Examples
>>> import numpy as np >>> from sklearn.datasets import make_sparse_coded_signal >>> from sklearn.decomposition import MiniBatchDictionaryLearning >>> X, dictionary, code = make_sparse_coded_signal( ... n_samples=100, n_components=15, n_features=20, n_nonzero_coefs=10, ... random_state=42) >>> dict_learner = MiniBatchDictionaryLearning( ... n_components=15, transform_algorithm='lasso_lars', random_state=42, ... ) >>> X_transformed = dict_learner.fit_transform(X)
We can check the level of sparsity of
X_transformed
:>>> np.mean(X_transformed == 0) 0.87...
We can compare the average squared euclidean norm of the reconstruction error of the sparse coded signal relative to the squared euclidean norm of the original signal:
>>> X_hat = X_transformed @ dict_learner.components_ >>> np.mean(np.sum((X_hat  X) ** 2, axis=1) / np.sum(X ** 2, axis=1)) 0.10...
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.
partial_fit
(X[, y, iter_offset])Updates the model using the data in X as a minibatch.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Encode the data as a sparse combination of the dictionary atoms.

fit
(X, y=None)[source]¶ Fit the model from data in X.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Training vector, where n_samples in the number of samples and n_features is the number of features.
 yIgnored
 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
 X{arraylike, sparse matrix, dataframe} of shape (n_samples, n_features)
Input samples.
 yndarray of shape (n_samples,), 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
 paramsmapping of string to any
Parameter names mapped to their values.

partial_fit
(X, y=None, iter_offset=None)[source]¶ Updates the model using the data in X as a minibatch.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Training vector, where n_samples in the number of samples and n_features is the number of features.
 yIgnored
 iter_offsetinteger, optional
The number of iteration on data batches that has been performed before this call to partial_fit. This is optional: if no number is passed, the memory of the object is used.
 Returns
 selfobject
Returns the instance itself.

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). 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
 selfobject
Estimator instance.

transform
(X)[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)
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