confusius.decomposition¶

Bases: _BaseFUSIDecomposer

Fast independent component analysis (ICA) for fUSI data.

The FastICA algorithm is based on Hyvarinen et al. (2000).

This estimator wraps sklearn.decomposition.FastICA while keeping xarray metadata through transform and inverse_transform:

• Input data are expected as (time, ...) where ... are spatial dimensions.
• transform returns a (time, component) DataArray of IC time courses.
• inverse_transform reconstructs to (time, ...) using the fitted spatial coordinates.

Two ICA modes are supported, selected via the mode parameter.

Spatial ICA (mode="spatial", default, matches FSL MELODIC): Maximises independence across the spatial dimension. The independent components are spatial maps; time courses are derived by projecting the data onto those maps. This is the appropriate choice when different brain regions are assumed to have independently fluctuating activity, which is the standard prior for network discovery. It is also better conditioned for fUSI data where the number of voxels greatly exceeds the number of time points.

Temporal ICA (mode="temporal"): Maximises independence across the time dimension. The independent components are temporal signals; the spatial maps are the corresponding mixing weights. Suitable when temporal independence is the relevant prior.

Parameters:

• n_components ¶

  (int, default: None ) –

  Number of components to use. If not provided, all are used.

• mode ¶

  ((spatial, temporal), default: "spatial" ) –

  Whether to find spatially or temporally independent components:

  • "spatial": fit on (voxels, time). The independent components are spatial maps; the projected time courses are their temporal mixing weights. Matches FSL MELODIC's default for single-subject data.
  • "temporal": fit on (time, voxels). The independent components are time courses; the spatial maps are their voxel-wise mixing weights.

• algorithm ¶

  ((parallel, deflation), default: "parallel" ) –

  Specify which algorithm to use for FastICA.

• whiten ¶

  ((unit - variance, arbitrary - variance), default: "unit-variance" ) –

  Specify the whitening strategy to use.

  • If "arbitrary-variance", a whitening with arbitrary variance is used.
  • If "unit-variance", the whitening matrix is rescaled to ensure that each recovered source has unit variance.
  • If False, the data are already considered whitened, and no whitening is performed.

• fun ¶

  ((logcosh, exp, cube), default: "logcosh" ) –

  The functional form of the G function used in the approximation to neg-entropy. Can be either "logcosh", "exp", or "cube".

  Default is "cube" to align with FSL MELODIC (pow3).

  You can also provide your own function. It should return a tuple containing the value of the function and its derivative at the point. The derivative should be averaged along its last dimension.

• fun_args ¶

  (dict[str, Any], default: None ) –

  Arguments to send to the functional form. If empty or None and fun="logcosh", fun_args defaults to {"alpha": 1.0}.

• max_iter ¶

  (int, default: 500 ) –

  Maximum number of iterations during fit. Default matches FSL MELODIC (--maxit=500).

• tol ¶

  (float, default: 1e-4 ) –

  A positive scalar giving the tolerance at which the un-mixing matrix is considered to have converged.

• w_init ¶

  ((n_components, n_components) numpy.ndarray, default: None ) –

  Initial un-mixing array. If not provided, values drawn from a normal distribution are used.

• whiten_solver ¶

  ((svd, eigh), default: "svd" ) –

  The solver to use for whitening.

  • "svd" is more stable numerically if the problem is degenerate, and often faster when n_samples <= n_features.
  • "eigh" is generally more memory efficient when n_samples >= n_features, and can be faster when n_samples >= 50 * n_features.

• random_state ¶

  (int, default: None ) –

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

• mask ¶

  (DataArray, default: None ) –

  Boolean spatial mask selecting voxels to include during fitting and projection. Must match the spatial dimensions and coordinates of the input data.

Attributes:

• maps_ ((n_components, ...) xarray.DataArray) –

  Spatial maps reshaped to the original spatial geometry.

  • In "spatial" mode: the independent component sources themselves, that is, the spatially independent patterns that FastICA found. These are the ICs in the strict sense.
  • In "temporal" mode: the unmixing directions in voxel space, that is, the spatial weights applied to each volume to extract IC time courses. The actual temporal ICs are returned by transform.
• mean_ ((...) xarray.DataArray) –

  Per-voxel empirical mean from the training set.

• whitening_ ((n_components, ...) xarray.DataArray) –

  Pre-whitening matrix reshaped to the original spatial geometry. Only present in "temporal" mode when whiten is not False.

• n_components_ (int) –

  Number of components estimated during fit.

• n_iter_ (int) –

  If algorithm="deflation", the maximum number of iterations run across all components. Otherwise, the number of iterations taken to converge.

• n_features_in_ (int) –

  Number of features seen during fit.

• feature_names_in_ ((n_features_in_,) numpy.ndarray) –

  Feature names seen during fit. Defined only when flattened feature labels are all strings.

Examples:

Spatial ICA (default, matches FSL MELODIC):

>>> import numpy as np
>>> import xarray as xr
>>> from confusius.decomposition import FastICA
>>>
>>> rng = np.random.default_rng(0)
>>> data = xr.DataArray(
...     rng.standard_normal((200, 5, 10, 20)),
...     dims=["time", "z", "y", "x"],
... )
>>>
>>> ica = FastICA(n_components=5, random_state=0)
>>> signals = ica.fit_transform(data)
>>> signals.dims
('time', 'component')
>>> reconstructed = ica.inverse_transform(signals)
>>> reconstructed.dims
('time', 'z', 'y', 'x')

Methods:

• fit –

  Fit FastICA on (time, ...) fUSI data.

• fit_transform –

  Fit on X and return transformed signals.

• get_metadata_routing –

  Get metadata routing of this object.

• get_params –

  Get parameters for this estimator.

• inverse_transform –

  Reconstruct data from component signals.

• set_output –

  Set output container.

• set_params –

  Set the parameters of this estimator.

• transform –

  Project data into component space.

fit(X: DataArray, y: None = None) -> FastICA

fit_transform(
    X: DataArray, y: None = None, **fit_params: object
) -> DataArray

get_metadata_routing()

get_params(deep=True)

inverse_transform(
    X: DataArray | NDArray[floating],
) -> DataArray

set_output(*, transform=None)

set_params(**params)

transform(X: DataArray) -> DataArray

Bases: _BaseFUSIDecomposer

Principal component analysis (PCA) for fUSI data.

Linear dimensionality reduction using singular value decomposition (SVD) of the data to project it to a lower dimensional space. The input data is centered but not scaled for each feature before applying the SVD.

It uses the LAPACK implementation of the full SVD or a randomized truncated SVD by the method of Halko et al. (2009), depending on the shape of the input data and the number of components to extract.

This estimator wraps sklearn.decomposition.PCA while keeping xarray metadata through transform and inverse_transform:

• Input data are expected as (time, ...) where ... are spatial dimensions.
• transform returns a (time, component) DataArray.
• inverse_transform reconstructs to (time, ...) using the fitted spatial coordinates.

Parameters:

• n_components ¶

  ((int, float or mle), default: None ) –

  Number of components to keep:

  • If n_components is not provided all components are kept: n_components == min(n_samples, n_features)
  • If n_components == "mle" and svd_solver == "full", Minka's MLE is used to guess the dimension. Use of n_components == "mle" will interpret svd_solver == "auto" as svd_solver == "full".
  • If 0 < n_components < 1 and svd_solver == "full", the number of components will be selected such that the amount of variance that needs to be explained is greater than the percentage specified by n_components.
  • If svd_solver == "arpack", the number of components must be strictly less than the minimum of n_features and n_samples. Hence, the None case results in: n_components == min(n_samples, n_features) - 1.

• whiten ¶

  (bool, default: False ) –

  Whether to multiply the loading vectors by the square root of n_samples and then divide them by the singular values to ensure uncorrelated outputs with unit component-wise variances.

  Whitening will remove some information from the transformed signal (the relative variance scales of the components) but can sometimes improve the predictive accuracy of the downstream estimators by making their data respect some hard-wired assumptions.

• svd_solver ¶

  ((auto, full, covariance_eigh, arpack, randomized), default: "auto" ) –
  • "auto": The solver is selected by a default policy based on data shape and n_components: if the input data has fewer than 1000 features and more than 10 times as many samples, then the "covariance_eigh" solver is used. Otherwise, if the input data is larger than 500x500 and the number of components to extract is lower than 80% of the smallest dimension of the data, then the more efficient "randomized" method is selected. Otherwise the exact "full" SVD is computed and optionally truncated afterwards.
  • "full": Run an exact full SVD calling the standard LAPACK solver via scipy.linalg.svd and select the components by postprocessing.
  • "covariance_eigh": Precompute the covariance matrix (on centered data), run a classical eigenvalue decomposition on the covariance matrix typically using LAPACK and select the components by postprocessing. This solver is very efficient for n_samples >> n_features and small n_features. It is, however, not tractable otherwise for large n_features (large memory footprint required to materialize the covariance matrix). Also note that compared to the "full" solver, this solver effectively doubles the condition number and is therefore less numerically stable (e.g. on input data with a large range of singular values).
  • "arpack": Run SVD truncated to n_components calling ARPACK solver via scipy.sparse.linalg.svds. It requires strictly 0 < n_components < min(X.shape).
  • "randomized": Run randomized SVD by the method of Halko et al.

• tol ¶

  (float, default: 0.0 ) –

  Tolerance for singular values computed by svd_solver == "arpack". Must be of range [0.0, infinity).

• iterated_power ¶

  (int or auto, default: "auto" ) –

  Number of iterations for the power method computed by svd_solver == "randomized". Must be of range [0, infinity).

• n_oversamples ¶

  (int, default: 10 ) –

  This parameter is only relevant when svd_solver="randomized". It corresponds to the additional number of random vectors to sample the range of X so as to ensure proper conditioning. See randomized_svd for more details.

• power_iteration_normalizer ¶

  ((auto, QR, LU, none), default: "auto" ) –

  Power iteration normalizer for randomized SVD solver. Not used by ARPACK. See randomized_svd for more details.

• random_state ¶

  (int, default: None ) –

  Used when the "arpack" or "randomized" solvers are used. Pass an int for reproducible results across multiple function calls.

• mode ¶

  ((temporal, spatial), default: "temporal" ) –

  Whether to fit PCA along temporal or spatial orientation:

  • "temporal": fit on (time, voxels). The principal axes are spatial maps; the projected components are temporal time courses that capture dominant variance across voxels.
  • "spatial": fit on (voxels, time). The principal axes are time courses; the projected components are spatial maps that capture dominant variance across time.

• mask ¶

  (DataArray, default: None ) –

  Boolean spatial mask selecting voxels to include during fitting and projection. Must match the spatial dimensions and coordinates of the input data.

Attributes:

• maps_ ((n_components, ...) xarray.DataArray) –

  Principal directions in feature space (loadings), representing the axes of maximum variance. Equivalently, the right singular vectors of the centered input data, parallel to its eigenvectors. Sorted by decreasing explained_variance_ and reshaped to the original spatial geometry.

• explained_variance_ ((n_components,) xarray.DataArray) –

  The amount of variance explained by each of the selected components. The variance estimation uses n_samples - 1 degrees of freedom. Equal to n_components largest eigenvalues of the covariance matrix of X.

• explained_variance_ratio_ ((n_components,) xarray.DataArray) –

  Percentage of variance explained by each of the selected components. If n_components is not set then all components are stored and the sum of the ratios is equal to 1.0.

• singular_values_ ((n_components,) xarray.DataArray) –

  The singular values corresponding to each of the selected components. The singular values are equal to the 2-norms of the n_components variables in the lower-dimensional space.

• mean_ ((...) xarray.DataArray) –

  Per-feature empirical mean, estimated from the training set. Equal to X.mean(axis=0).

• n_components_ (int) –

  The estimated number of components. When n_components is set to "mle" or a number between 0 and 1 (with svd_solver == "full") this number is estimated from input data. Otherwise it equals the parameter n_components, or the lesser value of n_features and n_samples if n_components is None.

• n_samples_ (int) –

  Number of samples in the training data.

• noise_variance_ (float) –

  The estimated noise covariance following the Probabilistic PCA model from Tipping and Bishop 1999. See "Pattern Recognition and Machine Learning" by C. Bishop, 12.2.1 p. 574 or http://www.miketipping.com/papers/met-mppca.pdf. It is required to compute the estimated data covariance and score samples. Equal to the average of min(n_features, n_samples) - n_components smallest eigenvalues of the covariance matrix of X.

• n_features_in_ (int) –

  Number of features seen during fit.

• feature_names_in_ ((n_features_in_,) numpy.ndarray) –

  Feature names seen during fit. Defined only when flattened feature labels are all strings.

Examples:

>>> import numpy as np
>>> import xarray as xr
>>> from confusius.decomposition import PCA
>>>
>>> rng = np.random.default_rng(0)
>>> data = xr.DataArray(
...     rng.standard_normal((200, 5, 10, 20)),
...     dims=["time", "z", "y", "x"],
... )
>>>
>>> pca = PCA(n_components=5, random_state=0)
>>> signals = pca.fit_transform(data)
>>> signals.dims
('time', 'component')
>>> reconstructed = pca.inverse_transform(signals)
>>> reconstructed.dims
('time', 'z', 'y', 'x')

Methods:

• fit –

  Fit PCA on (time, ...) fUSI data.

• fit_transform –

  Fit on X and return transformed signals.

• get_metadata_routing –

  Get metadata routing of this object.

• get_params –

  Get parameters for this estimator.

• inverse_transform –

  Reconstruct data from component signals.

• set_output –

  Set output container.

• set_params –

  Set the parameters of this estimator.

• transform –

  Project data into component space.

fit(X: DataArray, y: None = None) -> PCA

fit_transform(
    X: DataArray, y: None = None, **fit_params: object
) -> DataArray

get_metadata_routing()

get_params(deep=True)

inverse_transform(
    X: DataArray | NDArray[floating],
) -> DataArray

set_output(*, transform=None)

set_params(**params)

transform(X: DataArray) -> DataArray