Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.decomposition.DictLearning#

class nilearn.decomposition.DictLearning(n_components=20, n_epochs=1, alpha=10, reduction_ratio='auto', dict_init=None, random_state=None, batch_size=20, method='cd', mask=None, smoothing_fwhm=4, standardize=True, detrend=True, low_pass=None, high_pass=None, t_r=None, target_affine=None, target_shape=None, mask_strategy='epi', mask_args=None, n_jobs=1, verbose=0, memory=Memory(location=None), memory_level=0)[source]#

Perform a map learning algorithm based on spatial component sparsity, over a CanICA initialization [1]. This yields more stable maps than CanICA.

New in version 0.2.

Parameters
maskNiimg-like object or MultiNiftiMasker instance, optional

Mask to be used on data. If an instance of masker is passed, then its mask will be used. If no mask is given, it will be computed automatically by a MultiNiftiMasker with default parameters.

n_componentsint, optional

Number of components to extract. Default=20.

batch_sizeint, optional

The number of samples to take in each batch. Default=20.

n_epochsfloat, optional

Number of epochs the algorithm should run on the data. Default=1.

alphafloat, optional

Sparsity controlling parameter. Default=10.

dict_initNiimg-like object, optional

Initial estimation of dictionary maps. Would be computed from CanICA if not provided.

reduction_ratio‘auto’ or float between 0. and 1., optional

  • Between 0. or 1. : controls data reduction in the temporal domain. 1. means no reduction, < 1. calls for an SVD based reduction.

  • if set to ‘auto’, estimator will set the number of components per reduced session to be n_components. Default=’auto’.

method{‘cd’, ‘lars’}, optional

Coding method used by sklearn backend. Below are the possible values. 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. Default=’cd’.

random_stateint or RandomState, optional

Pseudo number generator state used for random sampling.

smoothing_fwhmfloat, optional.

If smoothing_fwhm is not None, it gives the full-width at half maximum in millimeters of the spatial smoothing to apply to the signal. Default=4mm.

standardizeboolean, optional

If standardize is True, the time-series are centered and normed: their variance is put to 1 in the time dimension. Default=True.

detrendboolean, optional

If detrend is True, the time-series will be detrended before components extraction. Default=True.

target_affine3x3 or 4x4 matrix, optional

This parameter is passed to image.resample_img. Please see the related documentation for details.

target_shape3-tuple of integers, optional

This parameter is passed to image.resample_img. Please see the related documentation for details.

low_passNone or float, optional

This parameter is passed to signal.clean. Please see the related documentation for details.

high_passNone or float, optional

This parameter is passed to signal.clean. Please see the related documentation for details.

t_rfloat, optional

This parameter is passed to signal.clean. Please see the related documentation for details.

mask_strategy{‘background’, ‘epi’, ‘whole-brain-template’,’gm-template’, ‘wm-template’}, optional

The strategy used to compute the mask:

  • ‘background’: Use this option if your images present a clear homogeneous background.

  • ‘epi’: Use this option if your images are raw EPI images

  • ‘whole-brain-template’: This will extract the whole-brain part of your data by resampling the MNI152 brain mask for your data’s field of view.

    Note

    This option is equivalent to the previous ‘template’ option which is now deprecated.

  • ‘gm-template’: This will extract the gray matter part of your data by resampling the corresponding MNI152 template for your data’s field of view.

    New in version 0.8.1.

  • ‘wm-template’: This will extract the white matter part of your data by resampling the corresponding MNI152 template for your data’s field of view.

    New in version 0.8.1.

Note

Depending on this value, the mask will be computed from nilearn.masking.compute_background_mask, nilearn.masking.compute_epi_mask, or nilearn.masking.compute_brain_mask.

Default=’epi’.

mask_argsdict, optional

If mask is None, these are additional parameters passed to masking.compute_background_mask or masking.compute_epi_mask to fine-tune mask computation. Please see the related documentation for details.

memoryinstance of joblib.Memory or string, optional

Used to cache the masking process. By default, no caching is done. If a string is given, it is the path to the caching directory.

memory_levelinteger, optional

Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Default=0.

n_jobsinteger, optional

The number of CPUs to use to do the computation. -1 means ‘all CPUs’, -2 ‘all CPUs but one’, and so on. Default=1.

verboseinteger, optional

Indicate the level of verbosity. By default, nothing is printed. Default=0.

References

1

Arthur Mensch, Gael Varoquaux, Bertrand Thirion, Compressed online dictionary learning for fast resting-state fMRI decomposition. IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016. pp. 1282-1285

Attributes
`components_`2D numpy array (n_components x n-voxels)

Masked dictionary components extracted from the input images.

Note

Use attribute components_img_ rather than manually unmasking components_ with masker_ attribute.

`components_img_`4D Nifti image

4D image giving the extracted components. Each 3D image is a component.

New in version 0.4.1.

`masker_`instance of MultiNiftiMasker

Masker used to filter and mask data as first step. If an instance of MultiNiftiMasker is given in mask parameter, this is a copy of it. Otherwise, a masker is created using the value of mask and other NiftiMasker related parameters as initialization.

`mask_img_`Niimg-like object

See http://nilearn.github.io/manipulating_images/input_output.html The mask of the data. If no mask was given at masker creation, contains the automatically computed mask.

__init__(n_components=20, n_epochs=1, alpha=10, reduction_ratio='auto', dict_init=None, random_state=None, batch_size=20, method='cd', mask=None, smoothing_fwhm=4, standardize=True, detrend=True, low_pass=None, high_pass=None, t_r=None, target_affine=None, target_shape=None, mask_strategy='epi', mask_args=None, n_jobs=1, verbose=0, memory=Memory(location=None), memory_level=0)[source]#
fit(imgs, y=None, confounds=None)[source]#

Compute the mask and the components across subjects

Parameters
imgslist of Niimg-like objects

See http://nilearn.github.io/manipulating_images/input_output.html Data on which the mask is calculated. If this is a list, the affine is considered the same for all.

confoundslist of CSV file paths or numpy.ndarrays or pandas DataFrames, optional

This parameter is passed to nilearn.signal.clean. Please see the related documentation for details. Should match with the list of imgs given.

Returns
selfobject

Returns the instance itself. Contains attributes listed at the object level.

fit_transform(X, y=None, **fit_params)#

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)#

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(loadings)[source]#

Use provided loadings to compute corresponding linear component combination in whole-brain voxel space

Parameters
loadingslist of numpy array (n_samples x n_components)

Component signals to transform back into voxel signals

Returns
reconstructed_imgslist of nibabel.Nifti1Image

For each loading, reconstructed Nifti1Image

score(imgs, confounds=None, per_component=False)[source]#

Score function based on explained variance on imgs.

Should only be used by DecompositionEstimator derived classes

Parameters
imgsiterable of Niimg-like objects

See http://nilearn.github.io/manipulating_images/input_output.html Data to be scored

confoundsCSV file path or numpy.ndarray or pandas DataFrame, optional

This parameter is passed to nilearn.signal.clean. Please see the related documentation for details

per_componentbool, optional

Specify whether the explained variance ratio is desired for each map or for the global set of components. Default=False.

Returns
scorefloat

Holds the score for each subjects. Score is two dimensional if per_component is True. First dimension is squeezed if the number of subjects is one

set_params(**params)#

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(imgs, confounds=None)[source]#

Project the data into a reduced representation

Parameters
imgsiterable of Niimg-like objects

See http://nilearn.github.io/manipulating_images/input_output.html Data to be projected

confoundsCSV file path or numpy.ndarray or pandas DataFrame, optional

This parameter is passed to nilearn.signal.clean. Please see the related documentation for details

Returns
loadingslist of 2D ndarray,

For each subject, each sample, loadings for each decomposition components shape: number of subjects * (number of scans, number of regions)

Examples using nilearn.decomposition.DictLearning#

Deriving spatial maps from group fMRI data using ICA and Dictionary Learning

Deriving spatial maps from group fMRI data using ICA and Dictionary Learning

Deriving spatial maps from group fMRI data using ICA and Dictionary Learning
Regions extraction using dictionary learning and functional connectomes

Regions extraction using dictionary learning and functional connectomes

Regions extraction using dictionary learning and functional connectomes