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.

fmralign.methods.SpectralOT

class fmralign.methods.SpectralOT(evecs, alpha=0.5, reg=0.01, max_iter=1000, tol=1e-07, verbose=False, backend='pot', device='cpu', **kwargs)[source]

Compute the optimal coupling between X and Y using an anatomy-aware cost matrix that combines functional and harmonic distances.

Parameters:
evecs(k, n_features) nd array

Harmonic embedding of the geometry, first k eigenmodes of the Laplace-Beltrami operator.

alphafloat

Trade-off parameter controlling the balance between functional data and the harmonic embedding evecs. Values should lie in the interval [0, 1], where higher values put more weight on the anatomy. Defaults to 0.5.

regfloat (optional)

Strength of the entropic regularization. Defaults to 0.01.

max_iterint (optional)

Maximum number of iterations. Defaults to 1000.

tolfloat (optional)

Tolerance for stopping criterion. Defaults to 1e-7.

verbosebool (optional)

Allow verbose output. Defaults to False.

backendstr

Backend to use for the OT solver. Can be either “pot” (Python Optimal Transport) or “geomloss” (GeomLoss library). Defaults to “pot”.

devicestr

Torch compatible device. Defaults to “cpu”.

kwargsdict

Additional arguments to be passed to the OT solver.

Attributes:
R(n_features, n_features) nd array (pot) or LinearOperator (geomloss)

Transport plan computed during fitting.

__init__(evecs, alpha=0.5, reg=0.01, max_iter=1000, tol=1e-07, verbose=False, backend='pot', device='cpu', **kwargs)[source]
fit(X, Y)[source]
Parameters:
X: (n_samples, n_features) nd array

source data

Y: (n_samples, n_features) nd array

target data

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_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

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.

set_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:
selfestimator instance

Estimator instance.

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(X)[source]