sklearn.decomposition.FastICA

class sklearn.decomposition.FastICA(n_components=None, *, algorithm='parallel', whiten='warn', fun='logcosh', fun_args=None, max_iter=200, tol=0.0001, w_init=None, random_state=None)[source]

FastICA: a fast algorithm for Independent Component Analysis.

The implementation is based on [1].

Read more in the User Guide.

Parameters
n_componentsint, default=None

Number of components to use. If None is passed, all are used.

algorithm{‘parallel’, ‘deflation’}, default=’parallel’

Apply parallel or deflational algorithm for FastICA.

whitenstr or bool, default=”warn”

Specify the whitening strategy to use. If ‘arbitrary-variance’ (default), a whitening with variance arbitrary is used. If ‘unit-variance’, the whitening matrix is rescaled to ensure that each recovered source has unit variance. If False, the data is already considered to be whitened, and no whitening is performed.

Deprecated since version 1.1: From version 1.3 whiten=’unit-variance’ will be used by default. whiten=True is deprecated from 1.1 and will raise ValueError in 1.3. Use whiten=arbitrary-variance instead.

fun{‘logcosh’, ‘exp’, ‘cube’} or callable, default=’logcosh’

The functional form of the G function used in the approximation to neg-entropy. Could be either ‘logcosh’, ‘exp’, or ‘cube’. You can also provide your own function. It should return a tuple containing the value of the function, and of its derivative, in the point. Example:

def my_g(x):
    return x ** 3, (3 * x ** 2).mean(axis=-1)
fun_argsdict, default=None

Arguments to send to the functional form. If empty and if fun=’logcosh’, fun_args will take value {‘alpha’ : 1.0}.

max_iterint, default=200

Maximum number of iterations during fit.

tolfloat, default=1e-4

Tolerance on update at each iteration.

w_initndarray of shape (n_components, n_components), default=None

The mixing matrix to be used to initialize the algorithm.

random_stateint, RandomState instance or None, default=None

Used to initialize w_init when not specified, with a normal distribution. Pass an int, for reproducible results across multiple function calls. See Glossary.

Attributes
components_ndarray of shape (n_components, n_features)

The linear operator to apply to the data to get the independent sources. This is equal to the unmixing matrix when whiten is False, and equal to np.dot(unmixing_matrix, self.whitening_) when whiten is True.

mixing_ndarray of shape (n_features, n_components)

The pseudo-inverse of components_. It is the linear operator that maps independent sources to the data.

mean_ndarray of shape(n_features,)

The mean over features. Only set if self.whiten is True.

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.

n_iter_int

If the algorithm is “deflation”, n_iter is the maximum number of iterations run across all components. Else they are just the number of iterations taken to converge.

whitening_ndarray of shape (n_components, n_features)

Only set if whiten is ‘True’. This is the pre-whitening matrix that projects data onto the first n_components principal components.

See also

PCA

Principal component analysis (PCA).

IncrementalPCA

Incremental principal components analysis (IPCA).

KernelPCA

Kernel Principal component analysis (KPCA).

MiniBatchSparsePCA

Mini-batch Sparse Principal Components Analysis.

SparsePCA

Sparse Principal Components Analysis (SparsePCA).

References

1

A. Hyvarinen and E. Oja, Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5), 2000, pp. 411-430.

Examples

>>> from sklearn.datasets import load_digits
>>> from sklearn.decomposition import FastICA
>>> X, _ = load_digits(return_X_y=True)
>>> transformer = FastICA(n_components=7,
...         random_state=0,
...         whiten='unit-variance')
>>> X_transformed = transformer.fit_transform(X)
>>> X_transformed.shape
(1797, 7)

Methods

fit(X[, y])

Fit the model to X.

fit_transform(X[, y])

Fit the model and recover the sources from X.

get_feature_names_out([input_features])

Get output feature names for transformation.

get_params([deep])

Get parameters for this estimator.

inverse_transform(X[, copy])

Transform the sources back to the mixed data (apply mixing matrix).

set_params(**params)

Set the parameters of this estimator.

transform(X[, copy])

Recover the sources from X (apply the unmixing matrix).

fit(X, y=None)[source]

Fit the model to X.

Parameters
Xarray-like of shape (n_samples, n_features)

Training data, 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
selfobject

Returns the instance itself.

fit_transform(X, y=None)[source]

Fit the model and recover the sources from X.

Parameters
Xarray-like of shape (n_samples, n_features)

Training data, 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)

Estimated sources obtained by transforming the data with the estimated unmixing matrix.

get_feature_names_out(input_features=None)[source]

Get output feature names for transformation.

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_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, copy=True)[source]

Transform the sources back to the mixed data (apply mixing matrix).

Parameters
Xarray-like of shape (n_samples, n_components)

Sources, where n_samples is the number of samples and n_components is the number of components.

copybool, default=True

If False, data passed to fit are overwritten. Defaults to True.

Returns
X_newndarray of shape (n_samples, n_features)

Reconstructed data obtained with the mixing matrix.

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, copy=True)[source]

Recover the sources from X (apply the unmixing matrix).

Parameters
Xarray-like of shape (n_samples, n_features)

Data to transform, where n_samples is the number of samples and n_features is the number of features.

copybool, default=True

If False, data passed to fit can be overwritten. Defaults to True.

Returns
X_newndarray of shape (n_samples, n_components)

Estimated sources obtained by transforming the data with the estimated unmixing matrix.

Examples using sklearn.decomposition.FastICA